Copyright © 2012 by Shawn Cole, Martin Kanz, and Leora Klapper

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Incentivizing Calculated Risk-Taking: Evidence from an Experiment with Commercial Bank Loan Officers Shawn Cole Martin Kanz Leora Klapper

Working Paper

13-002 July 5, 2012

Incentivizing Calculated Risk-Taking:Evidence from an Experiment withCommercial Bank Loan Officers

Shawn Cole, Martin Kanz, and Leora Klapper∗

July 5, 2012

Abstract

This paper uses a series of experiments with commercial bank loan officers to testthe effect of performance incentives on risk-assessment and lending decisions. Wefirst show that, while high-powered incentives lead to greater screening effort andmore profitable lending, their power is muted by both deferred compensation andthe limited liability typically enjoyed by credit officers. Second, we present directevidence that incentive contracts distort judgment and beliefs, even among trainedprofessionals with many years of experience. Loans evaluated under more permissiveincentive schemes are rated significantly less risky than the same loans evaluatedunder pay-for-performance.

JEL: D03, G21 J22, J33, L2Keywords: loan officer incentives, banking, emerging markets

∗Harvard Business School; World Bank; and World Bank, respectively. E-mails: scole@hbs.edu, mkanz@worldbank.org, andlklapper@worldbank.org. We wish to thank the cooperating financial institution for providing us with the data on loan applica-tions used in this paper. For helpful comments and suggestions, we thank Philippe Aghion, Andreas Fuster, Victoria Ivashina,Raj Iyer, Michael Kremer, Rohini Pande, Jose Liberti, Daniel Paravisini, Enrichetta Ravina, Andrei Shleifer, Antoinette Schoar,Erik Stafford, Jeremy Stein, and conference and seminar participants at Bocconi (Carefin), University of Chicago (Advanceswith Field Experiments), IGIDR Mumbai, IIM Bangalore, FIRS (Minneapolis), LBS European Winter Finance Conference2012, NBER Corporate Finance, NBER Risks of Financial Institutions, University of Virginia (Darden), and the World Bank.Samantha Bastian, Doug Randall, and Wentao Xiong provided excellent research assistance. Financial assistance from theKauffman Foundation, the International Growth Center, and the Harvard Business School Division of Faculty Support andResearch, is gratefully acknowledged.The opinions expressed do not necessarily represent the views of the World Bank, itsExecutive Directors, or the countries they represent.

“An evaluation of compensation practices at banking organizations preceding the financial crisis revealsthat they did, in fact, contribute to safety and soundness problems. Some firms gave loan officersincentives to write a lot of loans, without sufficient regard for the risks associated with those activities.The revenues that served as the basis for calculating bonuses were generated immediately, while therisks might not have been realized for months or years [...]. When these or similarly misaligned incentivecompensation arrangements were common in a firm, the very foundation of sound risk managementcould be undermined by the actions of employees seeking to maximize their own pay.”

—Daniel Tarullo, Board of Governors of the United States Federal Reserve1

1 Introduction

Following the global financial crisis, bank compensation has come under increased scrutiny.

While much of this attention has focused on incentives for top management, there is growing

recognition that non-equity incentives (such as commissions) provided to loan originators and

risk managers, may share some of the blame. Indeed, providing appropriate performance

incentives to employees at the lower tiers of a commercial bank’s corporate hierarchy is a

difficult problem: their very responsibility is to collect information that the bank cannot

otherwise observe, making monitoring difficult; they enjoy limited liability, and may have

different risk and time preferences than the bank’s shareholders.

This paper presents direct evidence on the effect of incentives on lending decisions and

risk-assessment. In a series of experiments, loan officers were paid to review and assess actual

loan applications, making over 14,000 lending decisions under exogenously assigned incentive

contracts. We evaluate three important classes of incentive schemes: (i) volume incentives

that reward origination, (ii) low-powered incentives that reward origination conditional on

performance, and (iii) high-powered incentives that reward performance and penalize default.

Our first set of results document the efficacy and limitations of performance incentives

in lending. We provide evidence that the structure of performance incentives strongly af-1In a speech entitled “Incentive Compensation, Risk Management, and Safety and Soundness” at the

University of Maryland Robert H. Smith School of Business. Washington, D.C., November 2, 2009.

1

fects screening effort, risk-assessment, and the profitability of originated loans. Loan officers

who are incentivized based on lending volume rather than the quality of their loan port-

folio originate more loans of lower average quality. By contrast, high-powered incentives

that reward loan performance and penalize bad lending decisions cause loan officers to ex-

ert greater screening effort, reduce exposure to loans with higher perceived ex-ante credit

risk, and induce significantly more profitable lending decisions while leading only to a small

reduction in lending volume. Relative to a baseline treatment with low-powered incentives,

high-powered incentives increase the probability that a bad loan is detected and increase

profits per originated loan by up to 3.5% of the median loan size; in contrast, origination

incentives lead to a substantial decline in the quality of originated loans and reduce profits

per loan by up to 5% of the median loan size.

Building on these results, we explore a number of constraints, inherent to any incentive

contract in lending, that may limit the efficacy of pay for performance. Consistent with the

predictions of a simple model of loan officer decision-making, we find that deferred compen-

sation attenuates the effectiveness of high-powered incentives. When incentive payments are

awarded with a three month delay, our measures of costly screening effort decline by between

5% and 14%, and we document a corresponding but less pronounced decline in the quality of

originated loans. Notably, we find that deferred compensation also moderates the negative

effect of incentive schemes that emphasize loan origination over the quality of originated

loans. Relaxing loan officers’ limited liability constraint (similar in spirit to giving a loan

officer equity in the loan) induces greater screening effort and leads to more conservative

lending decisions, but has only a moderate effect on the profitability of originated loans.

In our second set of results, we demonstrate that performance incentives also have impor-

tant effects on the subjective perception of credit risk. We find that loan officers evaluating

applications under performance contracts that provide strong incentives for approval system-

atically inflate internal ratings they assign to the loans they process. While internal ratings

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are strongly predictive of default under all incentive schemes, loan officers facing volume

incentives inflate risk ratings by as much as .3 standard deviations and irrespective of the

underlying asset quality. Loan officers inflate all features of the credit evaluation, including

measures that are difficult to quantify, such as the character of the borrower, as well as

metrics based on harder information such as interest coverage ratios. Since incentives affect

both risk ratings and approvals, the loan book approved under a permissive incentive scheme

may therefore be of poorer quality but, based on internal ratings alone, may in fact look less

risky than a set of comparable loans approved under a more conservative incentive scheme.

The use of actual loan applications, evaluated by experienced loan officers, allows us to

identify the impact of performance incentives on risk-assessment and actual risk-taking. By

design, our experimental set-up focuses on the lending decision, and allows us to isolate

the impact of performance pay on the quality of the initial screening decision from other

channels through which incentives may affect loan performance, such as the collection of soft

information or the degree of ex-post monitoring.2

This paper contributes to several literatures. The importance of incentives within the

firm has long been appreciated (see Baker, Jensen and Murphy (1988) for a review),3 yet

most empirical work that credibly identifies exogenous variation in performance incentives

has focused on relatively simple production tasks (see Lazear (2000); Bandiera, Barankay and

Rasul (2007, 2009, 2011); Kremer, Kaur and Mullainathan (2010)). We extend this literature2While client acquisition and monitoring are clearly important, the focus of our experiment is on the

impact of performance pay on screening incentives (for related evidence on screening incentives in sub-primeand small-enterprise lending, see Keys, Mukherjee, Seru and Vig (2010) and Agarwal and Ben-David (2012)).The experimental approach we pursue in this paper allows us to isolate the impact of performance pay onscreening behavior from other factors influencing loan origination and performance, such as informationproduction (Stein, 2002) and ex-post monitoring (Petersen and Rajan, 1994a). This distinction is alsomotivated by the fact that in the lending environment we study, the three stages of the lending process(collection of infomation and assembling information in the loan file, making a lending decision based onthis information and monitoring originated loans) are carried out by different employees, each facing theirown wage schedule.

3For evidence on team incentives, see Nalbantian and Schotter (1997) and Bandiera, Barankay and Rasul(2011), for more on rank order based incentives see Lazear and Rosen (1981).

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by focusing on a complex task in which unobservable effort is of paramount importance, and

where performance pay may affect productivity through a direct incentive effect as well as a

variety of behavioral channels, such as its impact on the subjective perception of credit risk.

Second, and relatedly, we contribute to the literature on incentive compensation and

risk-taking. The existing evidence in this area has focused almost exclusively on risk-taking

among CEOs and top executives (see, for example, Bebchuk and Spamann (2010), Bolton,

Mehran and Shapiro (2010), Edmans and Liu (2011) and Fahlenbach and Stulz (2012)).4

Balachandran, Kogut and Harnal (2011), for instance, show that during the recent financial

crisis, the prevalence of equity-based executive compensation at banks was associated with

a higher probability of the bank’s default. Mechanisms similar in their effect to equity

compensation have been proposed to align the incentives of employees at lower levels of a

bank’s hierarchy with those of the bank.

A growing literature highlights the importance of incentives for the production and trans-

mission of information for risk-management in commercial lending. Hertzberg, Liberti and

Paravisini (2010) demonstrate the presence of moral hazard in information revelation in an

Argentinian bank. Liberti and Mian (2009) show that hierarchical separation between loan

officers and their supervisors leads to greater reliance on hard information. Qian, Strahan

and Yang (2011) study the incentive effects of a policy reform in China that increased the

accountability of loan officers at state banks. They show that this non-pecuniary change in

loan officer incentives led to a significant improvement in the assessment of credit risk. Using

data from a large European bank, Berg, Puri and Rocholl (2012) show that in a setting where

loan approvals are based entirely on hard information, incentives that reward lending volume

lead loan officers to strategically manipulate applicant information for borrowers close to the

bank’s minimum threshold for approval. Finally, and most directly related to our study,4For more on performance pay among CEOs and top executives, see Jensen and Murphy (1990), Murphy

(1998) and Margiotta and Miller (2002). For a review and discussion of bank incentives in the context ofsub-prime lending, see also Bebchuk and Spamann (2010), Bebchuk et al. (2010) and Rajan (2010).

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Agarwal and Wang (2009) and Agarwal and Ben-David (2012) exploit an exogenous change

in the compensation structure of a bank in the United States to show that volume incentives

encourage excessive risk-taking and increase defaults.

The findings we report in this paper make the following three main contributions. First,

existing evidence on the effect of performance incentives in lending is limited to incentive

systems with relatively apparent flaws (such as an origination bonus). By contrast, our

experimental design allows us to test a range of incentive arrangements, including those

which are closer to an optimal contract. We offer the first empirical measurement and

quantification of two important constraints that prevent first-best contracting in a lending

setting: deferred compensation and limited liability. Additionally, our experimental design

tracks aspects of loan officer behavior that would normally be unobservable to a bank or

econometrician, and allows for the direct measurement of the relationship between incentives,

effort, and loan performance.

Second, we present new evidence that performance incentives can distort the assessment

of credit risk, even when internal ratings are not tied to loan officer compensation. The find-

ing that loan officers change their assessment of credit risk in response to monetary incentives

is consistent with the psychological concept of cognitive dissonance (Akerlof and Dickens,

1982) and resonates with stylized evidence from sub-prime lending (Barberis, 2012): loan

officers manipulate their beliefs about loans they review because they are not comfortable

thinking that the loans they wish to approve under prevailing incentives are indeed of poor

quality. This “wishful thinking” effect is also similar to the one identified by Moore, Tanlu

and Bazerman (2003) and Mayraz (2012) in laboratory experiments in which participants

assigned to the role of the buyer in a fictitious market reported lower private valuations and

predictions of future asset prices than those assigned to the role of the seller.

Finally, this paper contributes to the literature on lending in informationally opaque

credit markets. We examine the role of loan officer effort and risk-assessment in an envi-

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ronment of high idiosyncratic risk. This is related to, but distinct from, the special role

played by loan officers in collecting soft information, and monitoring borrowers following the

disbursal of a loan (see Petersen and Rajan (1994b), Berger and Udell (1995), and Berger,

Klapper and Udell (2001))5. The separation of information collection and approval decisions

is common for a wide range of financial products, and is especially relevant in emerging

markets for two reasons. First, the small ticket size of consumer and small-enterprise loans

in these markets typically rules out the use of a costly, relationship-intensive lending model

so that the collection of customer information for small-ticket loans is often outsourced to

third parties. Second, regulation often constrains the degree of decentralization of foreign

lenders (see Mian (2006)). This further limits the use of relationship lending, especially for

smaller loans, and places greater importance on risk-management at the time of the initial

screening decision, which is the focus of our analysis in this paper.

The remainder of the paper proceeds as follows. In the next two Sections we discuss the

basic incentive problem, and describe the incentive schemes we test and how they relate to a

simple theoretical model of loan officer decision makig. Section 3 describes our experimental

set-up, and section 4 presents our main results. Section 5 concludes.

2 Performance Incentives in Lending

The potential for excessive and socially inefficient risk-taking in response to poorly designed

incentive schemes has long been recognized. However, first-best incentive contracts may not

be implementable in many real world settings. Pay for performance schemes require easily

quantifiable criteria against which to measure and reward performance, which may be difficult

or impossible to define for complex tasks, such as loan origination. In many organizational

settings, the implementation of optimal incentive contracts is further constrained by multi-5For more empirical evidence on lending relationships see also Harhoff and Korting (1998) and Santikian

(2011). For a survey of the literature on relationship lending see Boot (2000).

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tasking concerns, the difficulty in observing effort, and the tradeoff between the desire to

balance incentives and insurance against negative outcomes.6

The basic incentive problem in lending arises from the fact that loan officers are tasked

with allocating the bank’s capital based on private information and risk-assessments that

are usually unobservable to the bank (Udell (1989), Berger and Udell (2002), Stein (2002)).

In this setting, several important constraints preclude the implementation of a contract that

would align the incentives of the bank’s shareholders with those of its employees by making

a loan officer the fully liable residual claimant of the loan.

First, loan officer effort is typically unobservable to the bank. Second, loan officers

are by necessity protected by limited liability, since they take decisions on large amounts

of money, which typically far exceed the amount of any penalty a bank could enforce to

penalize bad lending decisions. Third, loan officers may have a different time horizon and a

higher discount rate than the bank. It may therefore be more expensive to generate effort

with deferred pay, and pay that is conditioned on loan outcomes, than with an immediate

performance bonus. Finally, in contrast to production tasks with a clear relationship between

input and outcome, the assessment of credit involves a complex tradeoff between risk and

return in an environment with high aggregate and idiosyncratic risk. This makes it difficult

to reliably identify idiosyncratic defaults and further complicates the use of (noisy) realized

outcomes as a benchmark for the measurement of loan officer effort and performance.

While these constraints are present in any lending environment, they are likely to bind

much more severely in emerging credit markets, characterized by severe information asym-

metries, limited credit information and poor enforceability of debt contracts. Banks in this

environment are thus particularly reliant on the risk-assessment of their front-line employees,

which in turn introduces significant scope for moral hazard and agency conflict within the

lending institution (see, for example, Liberti (2005), Liberti and Mian (2009)).6See Gibbons (1998) for a review.

7

Where banks provide performance incentives, loan officer compensation usually consists of

a fixed base salary and a performance component. This performance component may place

weight on lending volume, ex-post loan performance, or a combination of the two.7 The

recent debate on bank compensation has focused on two key features of such incentive

contracts in lending: First, the incentive power of the contract, which affects how sensitive

loan officers are to the costs of default; and the often short time-horizon of compensation,

which may lead loan officers to focus on short-term gains rather than long-term value. In

the experiments reported in this paper we vary both dimensions, and demonstrate that both

have important implications for risk-assessment and lending decisions in a sample of highly-

experienced loan officers.8 In addition, we provide novel evidence on behavioral effects that

may limit the efficacy of loan officer incentives as a tool for managing credit risk.

As a framework for the empirical analysis, we develop in the appendix a simple model

of performance incentives and loan officer decision-making. In the model, a loan officer may

exert costly effort to obtain a signal about the quality of a proposed loan. The model makes

four simple predictions about the effect of performance incentives on loan officer behavior.

First, an origination bonus scheme as often employed by commercial banks, which incen-

tivized lending volume without penalizing default leads to indiscriminate lending, low effort

and high defaults. By contrast, high-powered incentives that reward profitable decisions and

penalize default result in greater screening effort but more conservative lending decisions.

Second, deferring compensation reduces the power of performance-based incentives. Third,

relaxing a loan officer’s limited liability constraint increases effort. Finally, intrinsically mo-

tivated loan officers exert more effort.7In many cases, the contract also includes a team performance component. To limit multi-tasking con-

cerns, our experiments focus on changes to the individual performance component of the contract.8Heider and Inderst (2011) develop a model relating the choice of loan officer incentives to the severity of

the bank’s internal agency problem which is in turn determined by the banks’ competitive position. In ourexperiments, we abstract from the bank’s optimal choice of incentive scheme and focus on the loan officer’s(behavioral) response to a menu of commonly implemented incentive contracts.

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In the empirical section of this paper, we take these predictions to the data and additionally

study the effect of performance incentives on loan officers perception of credit risk. We

find that volume incentives lead loan officers to inflate internal ratings, even when these

ratings are not tied to the loan officer’s evaluation or incentive scheme, while high-powered

incentive contracts lead to a more accurate assessment of credit risk and more profitable

lending decisions.

3 Experimental Incentive Schemes

We test the predictions of the model (see appendix) using a framed field experiment, in

which commercial bank loan officers evaluate loan applications from the client database of a

large Indian bank. To test the impact of performance contracts on loan officer behavior, we

exogenously vary the incentive power, the time horizon over which performance incentives

are paid, and the degree of limited liability enjoyed by a loan officer. We vary the power of

the incentive contract by assigning loan officers to contracts that specify three conditional

payments: a payment wP made when a loan is approved and performs, a payment wD, made

when a loan is approved and defaults and a payment w that is made when a loan is declined.

Because the outcome of a loan is only observed with some delay, performance incentives,

in practice, must be paid with a lag. In our setting, under the non-deferred payment scheme,

incentives were paid immediately following an experimental session. In the deferred compen-

sation scheme, incentive payments were delayed three months, with the loan officers given a

choice of returning to collect the payment, or receiving a check in the mail. This allows for

two contrasts: from the policy perspective, comparing deferred-compensation, performance

based pay to immediate compensation based on origination.

Finally, we experimentally relax loan officers’ limited liability constraint, by providing an

initial endowment that the participant can lose if he approves a number of non-performing

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loans. This mimics proposed “clawback” schemes. Throughout the paper, we express ex-

perimental incentive contracts as as the vector w = [wP , wD, w]. In addition to these three

performance-based conditional payments, loan officers received an unconditional show-up fee

of Rs 100 (US$ 2.25), each time they participated in a session of the experiment. To ensure

that participants perceived conditional payoffs as salient, we calibrated the mean payout of

experimental incentive schemes to roughly 1.5 times the hourly wage of the median partic-

ipant in our experiment, a Level II public sector credit officer with an annual income of Rs

240,000 (US$ 4,800) and an approximate hourly wage of Rs 125 (US$ 2.5).

Because understanding the impact of monetary incentives on (costly) screening effort

was a main objective of the experiment, half of our sessions included a “costly information”

feature. In this condition, loan officers were given an initial information endowment of Rs

108. In the “costly information” setting, loan officers were able to review only basic client

and loan information items for free (two out of nine sections of the loan application) and

were charged Rs 3 per section for as many of the remaining loan file sections as they chose

to view. In these sessions, loan officers received their remaining information endowment in

cash at the conclusion of the session, in addition to their incentive payments.

Table I summarizes the experimental incentive schemes. An obvious feature of incentive

schemes C is that a (weakly) dominant strategy from the participants perspective is to

exert no effort screening, and accept every loan application. This corresponds to a volume

“origination piece rate” often observed in consumer lending. In addition to these schemes,

the research staff ran two additional schemes which do not mimic real-world schemes. Scheme

D paid Rs 50 if a loan performed, and zero if it defaulted or was rejected. Scheme E paid

Rs 100 (US$ 2.25) if a loan performed, and 0 if it defaulted or rejected. As these schemes

do not have real-world counterparts, and were run on a reduced sample, we do not focus on

them in this paper. However, complete results are reported in the supplementary appendix.

We test the following predictions. First, incentives awarded for origination will lead

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to excessive risk-taking. Indeed, purely rational and profit-maximizing loan officers should

indiscriminately approve all applications under scheme C, and exert no screening effort.

Second, high-powered incentives will increase effort by increasing the rewards for a profitable

lending decision and increasing the penalty for originating a loan that ultimately becomes

delinquent. Thus, the amount of effort exerted under various treatment can be ranked B

> A > C. Third, high-powered incentives will induce more conservative lending behavior

by increasing the cost to the loan officer of making a bad lending decision. Fourth, if a

loan officer’s discount rate is greater than zero, the amount of effort induced by deferred

compensation will be less than the amount of effort induced by A and B. Finally, if credit

officers are intrinsically motivated, or believe that their performance on this task may affect

their reputation, they may invest in screening loan applications even when such scrutiny will

not yield additional remuneration.

4 Experimental Context and Design

4.1 Experimental Task

The experiment is designed to closely match the lending process for low-documentation small

business loans, a class of loans for which the accurate assessment of credit risk depends

crucially on the judgment of the bank’s loan officers. Loan officers recruited from leading

Indian commercial banks visit our labs outside of work hours to evaluate credit applications.

The credit application files were obtained from a leading commercial lender in India, and are

described below. Participants evaluate these applications, complete a risk evaluation form,

and make a recommendation about whether the loan should be approved.

While the lending decisions are hypothetical, in the sense that loans underlying the

experiment have been previously made and their outcome is observed, participants receive

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only information that was available to the bank at the original time of application. Because

we observe the performance of loans in our data set, we can pay participants performance

incentives based on their decision and the actual outcome of the loan. Our sample of loans

consists of unsecured small-business working capital loans with a ticket size of less than Rs

500,000 (US$ 10,000). Sales and origination channels for this class of loans are generally

distinct, so that (analogous to low-documentation loans in the United States) loans are

sourced by a bank’s sales agents who collect client information, which is then forwarded to a

credit officer for approval. The task faced by the bank’s credit officers is to screen and make

profitable lending decisions based on the information contained in an applicant’s loan file.

The applicant information contained in the loan files is “hard” in the sense that it can be

transcribed. However, borrower information differs in its degree of verifiability, and ranges

from audited financials to information that requires a significant degree of interpretation,

such as trade reference reports or a description of the applicant’s business (see Petersen (2004)

for a discussion). Although regulators require banks to collect an applicant’s tax record and

audited income statement, this information is often unreliable and difficult to verify. New

applicants typically lack a credit score or established record of formal borrowing, which rules

out the use of predictive credit scoring, “scorecard lending” and other more systematic loan

approval technologies. Because the ticket size of a representative small business loan is small,

relative to the fixed cost of underwriting, risk-management occurs primarily through ex-ante

screening, rather than prohibitively costly relationship lending or ex-post monitoring.

4.2 Loan Database

As a basis for the experiment, we requested a random sample of loan applications from a large

commercial lender in India (hereafter “the Lender”), and received 650 loan files. The loan files

contain all information available to the Lender at the time the application was processed, as

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well as at least nine months of repayment history for each loan9. The information contained

in each loan application can be grouped into the following categories, corresponding to the

sections of the Lender’s standard application format: (1) basic client information including

a detailed description of the client’s business, (2) documents and verification (3) balance

sheet and (4) income statement. In addition, participants in the experiment had access to

three types of background checks for each applicant: (5) a site visit report on the applicant’s

business, (6) a site visit report on the applicant’s residence and (7) a credit bureau report,

available for 66% of all applicants. We limit our attention to uncollateralized small business

loans to self-employed individuals, with a ticket size between Rs 150,000 (US$ 3,000) and Rs

500,000 (US$ 10,000).10 Loan file summary statistics are reported in Table III. We consider

only term loans to new borrowers, many of whom are first-time applicants for a formal

sector loan.11 The median loan in our database has a tenure of 36 months, a ticket size of

Rs 283,214 (US$ 6,383) and a monthly installment of Rs 9,228. (US$ 208).

To rule out vintage effects and ensure consistency in the initial screening standards ap-

plied to loans used in the experiment, we restrict our sample to loans originated in 2009

Q1 and 2009 Q2. Based on the Lender’s proprietary data on the repayment history of each

loan, we then classified credit files into performing and non-performing loans. Following the

standard definition, we classify a loan as delinquent if it has missed two monthly payments

and remains 60+ days overdue, and as performing otherwise. To calculate profitability to9More than 90% of all defaults occur during the first five months of a loan’s tenure, so that our default

measure allows for a relatively precise measurement of loan quality.10While none of the loans in the experiment carried any collateral security, borrowers faced strong incentives

for repayment. First, the Lender routinely offers follow-up loans at reduced interest rates to clients with agood repayment history. Second, borrowers who default on their loans are reported to India’s main creditbureau, implying a credible threat of future exclusion from bank credit. Loans that remain unsettled for90+ days are classified as non-performing (NPA), reported to the credit bureau, and referred to the Lender’scollections department. A small fraction of loans in the overdue category are restructured in negotiationbetween clients and the Lender. To rule out selection bias, our sample excludes repeat borrowers andrestructured loans.

11Since none of the loans in our sample are collateralized, they are priced at an annual interest rate ofbetween 15 and 30 per cent. We control for the variation in interest rates by including loan fixed effects.

13

the bank, we subtract the disbursal amount from the discounted stream of repayments.12 To

achieve as representative a sample as possible, we also include 26 credit files from clients who

applied, but were turned down by the Lender. For the incentive payouts in our study, these

rejected loans are treated as non-performing13. Throughout the analysis, we report results

disaggregated by non-performing and declined loans and show that our results are unaffected

by the classification of loans declined ex-ante by the Lender. Importantly, the final columns

of Table III show that loan files contain information that may be useful in distinguishing

good loans and bad loans, suggesting there are returns to effort in this setting.

4.3 Loan Officers and Experimental Procedure

Loan officers were recruited from the active staff of several leading private and public sector

commercial banks. Participants first attended an introductory presentation, completed a

practice session, and then participated in up to 15 sessions of the experiment, in which they

evaluated six loan applications per session. Working conditions and presentation of informa-

tion were designed to closely resemble the actual work environment of the representative loan

officer.14 Experimental sessions were scheduled to last one hour, although participants could

finish early or late if they so chose. Incentive treatments, as described in Section 2, were

randomly and individually assigned at the loan officer and session level, such that officers

evaluated a batch of six loans under a given scheme.15 The experiments took over a year

to complete, and not all incentive schemes were eligible to be assigned in any given session.12We estimate the Lender’s net profit per loan as the net present value of the disbursal plus repayments

including interest, discounted by 8%, the approximate rate on Indian commercial paper between January 1and December 31, 2009, and assuming a 10% recovery on defaulted loans.

13However, we do not include initially rejected loans in the profitability calculations. In non-reportedresults we also verified that our analysis is robust to the exclusion of rejected loans.

14Harrison, List and Towe (2007) point out that laboratory behavior may not match field behavior wheneliciting risk attitudes (“background risk”). In contrast to that study, we use within-subject variation,and the inclusion of loan officer fixed-effects may reduce the importance of heterogeneous perceptions ofbackground risk from different subjects.

15The number of loan files was held constant to rule out multitasking concerns.

14

Hence, our regressions include a set of fixed effects to control for these randomization strata.

At the start of each session, loan officers were assigned to an incentive treatment, received

a one-on-one introduction to the incentive scheme and completed a short questionnaire to

verify comprehension. We report summary statistics for the population of participating loan

officers in Table II, Columns (1) to (4). The median loan officer in our sample is a Level II

public sector bank employee who is 35 years old, and has 10 years of work experience. In

Table II, Columns (5) to (8) we report comparable characteristics from a sample of all loan

officers from a major bank in the state in which our experiment takes place. Our sample is

quite representative of this reference population in terms of age, rank and experience.

A customized software interface reproduced each section of the loan application on a

separate tab: a description of the applicant’s business, balance sheet, trade reference, site

visit report, document verification and credit bureau report when available. While reviewing

this information, participants were asked to assess the applicant’s credit risk using a form

adapted from the standard format of a leading Indian commercial bank, with categories for

personal risk, business risk, management risk and financial risk. The risk ratings serve three

purposes: first, they add realism to the lab session, as completing an internal risk rating is a

routine part of evaluating applications; second, they allow us to elicit a measure of perceived

credit risk that is not tied to loan officer compensation. Finally, internal ratings serve to

assist the loan officer in aggregating information about the application in a systematic way.

Within each experimental session, the sequence of loan files was randomly assigned, but the

ratio of performing, non-performing and declined loans was held constant at four performing

loans, one non-performing loan and one loan declined by the Lender.16 Loan officers were

asked to evaluate these loans based on their best judgment, but were not given information

about the ratio of good and bad loans or the outcome of any particular loan under evaluation.16We chose this ratio to match responses to a pilot survey, in which we elicited the expected distribution

of performing, non-performing and ex-ante declined loans for loans of the type used in the experiment.

15

5 Empirical Strategy and Results

Since treatment status was randomly assigned, our empirical strategy is straightforward and

we estimate treatment regressions of the form:

yil =K−1∑k=1

βkTilk + θi + θl + ζ ′Ril + ξ′Xil + εil (1)

where yil is the outcome of interest for loan officer i and loan l, Til is a vector of treatment

dummies for the incentive schemes being compared to the baseline. In all regressions, we use

the low-powered baseline incentive wB = [20, 0, 10] as the omitted category. We additionally

control for loan officer fixed effects, θi, loan file fixed effects θl, and individual controls Xil,

including loan officer age, seniority, rank, education, and include dummies for whether the

loan officer has management and business experience. Finally, we include dummies for the

randomization strata, Ril. Standard errors are clustered at the loan officer-session level,17

the same level at which the treatment is assigned.

Our data set includes 14,369 lending decisions, representing 206 unique subjects, with

three key treatment conditions: (1) Low-powered incentives, which we use as the baseline

throughout the empirical analysis; (2) High-powered incentives, which reward loan officers

for approving loans that perform and penalizes the origination of loans that default; and (3)

Origination bonus, which rewards the loan officer for every originated loan.18

In addition to these incentive vectors, we vary conditions under which incentives are paid.

In 369 randomly selected sessions (2,214 loan evaluations), we defer incentive payments by 3

months, rather than paying immediately. In further 163 sessions (978 evaluations), we relax

the participant’s limited liability constraint by providing an initial information endowment

of Rs 200 (US$ 4.5), which can be lost if a loan officer makes a series of unprofitable lending17Each session consists of one loan officer evaluating six files.18Regressions using all data we collected, which includes the performance bonus schemes which pay only if

a loan performs, along with the appropriate treatment dummies, are reported in the supplementary appendix.

16

decisions. Finally, in 137 sessions (3,638 loans), we provide loan officers with an initial

information endowment of Rs 108 (US$ 2.25), which they may spend to sections of the loan

file. Table I summarizes the sample sizes used for contrasts. Table OA.I in the supplementary

appendix reports a test of random assignment that compares loan officer characteristics

across treatments, and confirms that the randomization was successful.

To test our hypotheses, we consider three groups of outcomes: (i) measures of screening

effort, (ii) measures of subjective risk-assessment, and (iii) lending decisions (actual risk-

taking) and the resulting profitability of originated loans. We construct two measures of

screening effort: the number of credit file sections reviewed by a credit officer; and the amount

of money spent on reviewing additional information under the costly-information treatment.

To measure risk-assessment and risk-taking, we record internal risk ratings assigned to each

loan. Finally, to evaluate loan officer decisions and performance, we match the loan officer’s

lending decision to the actual profitability of the loan to the financial institution.

5.1 Descriptive Statistics

Before turning to the main analysis, we report descriptive statistics of loan evaluations during

the exercise. We first verify that the experimental task is meaningful, in the sense that it is

indeed possible for loan officers to infer credit risk based on hard information contained in

an applicant’s loan file. To do this, Table III presents mean comparisons of loan application

information for performing and non-performing loans. As is evident from the test statistics

comparing hard information characteristics of performing and non-performing loans, there

are a number of systematic differences in these loan characteristics that help distinguish

ex-post profitable from ex-post defaulting loans. In particular, borrowers who defaulted on

their loans had substantially lower revenue, younger businesses, higher ratios of monthly

debt service to income, compared to borrowers who remained current on their obligations.

17

Overdues on credit reports also predicted default. Higher-quality borrowers did report higher

levels of debt, a fact consistent with the common observation of low-quality borrowers being

excluded from formal financial markets.

Table IV reports summary statistics of loan evaluations by loan type and incentive.

We note the following. First, even for a group of highly experienced loan officers, making

profitable lending decisions in this informationally challenging lending environment was not

a trivial task. On average, loan officers approved 75% of all loans evaluated in the experiment

and made correct lending decisions in 65% of all cases. Lending decisions were, however,

profitable under all incentive schemes in the experiment and would have earned the bank

an average net present value of US$ 240 (5.9% of the median loan size) per originated

loan. Lending volume responds dramatically to incentives. Identifying performing loans

was substantially easier than identifying non-performing loans or loans that were rejected

by the Lender ex-ante. Changes in the incentive power of the contract were especially

effective in improving loan officer’s success in detecting non-performing loans (Column (9)).

These patterns are directly reflected in the profitability of loans approved under alternative

incentive schemes (Column (6)).

Table IV Column (2) describes the number of sections a loan officer reviewed prior to

making a decision, while Column (3) gives this number for only the subsample which was

charged to see additional sections from the loan file. Virtually all loan officers study the

basic information and borrower profile sections. However, some chose to reject or accept a

loan prior to viewing the entire application, particularly when the incentive scheme did not

reward higher-quality screening. When information was costly, loan officers were most likely

to pay for income statements and balance sheet information, and much less likely to pay for

the site visit reports (results not reported in table).

In addition to observed lending decisions, we analyze loan officer risk assessment, as

measured by the rating each loan officer gave to each loan. Since these ratings themselves

18

were not incentivized, one might be concerned about whether they contain useful information.

We report formal tests of the information content of internal ratings in Table OA.III in the

supplementary appendix. The results show that loan officer assessments of credit risk are a

meaningful and strongly significant predictor of actual lending decisions, the probability of

default and the profit of loans evaluated in the experiment. This is true for the overall rating

(Table OA.III, Panel A) as well as its sub-components measuring an applicant’s perceived

personal and financial risk (Table OA.III, Panel B and C). A Kolgomorov-Smirnov test of

the equality of ratings for performing versus non-performing loans rejects at the 1% level.

Since loan officers complete multiple sessions, one might wonder whether loan officers

learn over the course of the study. An affirmative answer might be cause for concern, given

that our average loan officer has over 13 years of experience lending. To verify that learning

over the course of the exercise poses no threat to the validity of our results, Figure 2 plots

the average fraction of correct decisions and average profit per originated loan as a function

of the number of completed experimental sessions. These demonstrate no learning effect, a

result confirmed by regression results in Table OA.II in the supplementary appendix.

5.2 Incentivizing Screening Effort

We first analyze the effect of incentives on screening effort. Intuitively, performance incen-

tives can affect the quality of lending decisions if they induce a loan officer to choose higher

screening effort, translating into either the collection of borrower information that was not

previously available or a more thorough evaluation of available information. The design of

our experiment provides us with a straightforward measure of screening effort, namely, the

thoroughness with which the loan officer reviews the loan file. Specifically, we record how

many of the ten sections of the credit file the loan officer chooses to review before mak-

ing a decision. In a separate set of sub-treatments meant to make the effort trade-off even

19

more stark to loan officers, we charge loan officers Rs 3 for each section of the loan dossier

beyond what would be available on the application form19. As human subject considera-

tions precluded an experimental design in which loan officers paid us to participate, in these

treatments we provide each loan officer with an initial information endowment of Rs 108

(approximately US$ 2.25 per experimental session). Participants could choose not to pay to

view additional tabs, in which case Rs 108 would be paid to them at the end of the session,

in addition to whatever show-up and incentive payments they earn. This information cost

was not trivial: purchasing access to all six tabs would cost close to the maximum payout

of 20 under the low-powered and origination incentive schemes. We use the amount spent

to view loan sections as a second measure of screening effort, capturing the notion of costly

information. Because screening effort is not observable to the bank, we do not tie bonus

payments to measures of observed effort.

Table V reports the effect of performance pay on screening effort, measured by the number

of loan file sections reviewed when the only cost of effort was the loan officer’s time (Columns

(1) and (2)), as well as when the loan officer was required to pay to view additional tabs

(Columns (3) and (4)). High-powered incentives significantly increase screening effort. On

average, loan officers facing high-powered incentives viewed .4 additional tabs of information

when there was no charge to view tabs (the mean number of tabs viewed was 5.06 when

information was free, and 3.99 when information was costly). When information was costly,

high-powered incentives had an even stronger effect, increasing the average number of tabs

viewed by .8-1.2. These effects are statistically significant across all specifications. Interest-

ingly, we do not observe effort to be significantly lower when loan officers face origination

bonuses, although the standard errors are not small enough to rule out meaningful effects.

These results confirm that loan officers respond strongly to monetary incentives, and suggest19Available for free were basic applicant details and documentation provided by the borrower. Loan officers

paid for income statement and balance sheet information, residential and business site visit reports, and tradeand credit reference checks.

20

that performance pay can incentivize effort in the review of borrower information.

5.3 Risk-Assessment and Risk-Taking

How do performance incentives affect the perception of credit risk and actual risk-taking?

Risk-assessment is easy to measure, as each loan officer is required to enter an internal rating

for each loan they evaluate. Before participants made a decision to approve or decline a loan,

they were asked to assess the merit of the application along 15 credit-scoring criteria adapted

from the standard internal credit scoring format of an Indian bank. Internal ratings range

from 0 to 100 and a higher score indicates higher credit quality, i.e. lower default risk.

Internal ratings were not binding for the loan officer’s decision. That is, an applicant did

not have to attain a minimum score to be considered for a loan. This approach is common

practice for consumer and small-enterprise loans for which the bank records internal ratings

but does not use predictive credit scoring in the approval process.

In Table VI we first present evidence on the effect of incentives on the perception of

credit risk. We find strong evidence that the structure of performance incentives distorts

the subjective assessment of credit risk. Loan officers facing incentives that reward loan

origination inflate internal ratings by as much as .16 standard deviations. In the specification

with loan officer and loan fixed effects (Table VI, Column (2)), we see that the size of the

coefficient increases in direct proportion to the incentive that is placed on origination. This

is particularly striking for the treatments that paid Rs 50 or Rs 100 (US$ 2.25) only if a loan

was made and performed, and zero otherwise. Facing this incentive scheme, loan officers

inflate internal ratings by up to .3 standard deviations (significant at the 5 percent or better

level across all specifications). Taken together, these findings provide strong evidence in

support of a behavioral view of performance pay in lending as suggested, for example, by

Barberis (2012): incentives that reward origination do not merely affect the propensity to

21

take on risk, but in fact distort loan officer judgment and the perception of credit risk.

We next turn to the effect of performance pay on risk-taking. Because the realized

outcome of a loan may be a poor proxy of the ex-ante riskiness at the time a loan is originated,

we construct a measure of ex-ante risk, by averaging the internal ratings of all loan officers

who observed a given file under the baseline incentive. We call this the “loan’s average

rating.” We also calculate the coefficient of variation for the baseline internal score, which

is a measure of the degree of disagreement of loan officers about the riskiness of the loan.

If high-powered incentives encourage more discerning lending decisions, they will lead loan

officers to approve loans with higher average rating and a lower variance. (Indeed, in our

data set, the coefficient of variation is strongly correlated with default.)

Table VII tests this hypothesis. Rather than using the loan outcome, which is a noisy

measure and depends on idiosyncratic risk, we take advantage of the fact that we had over

100 loan officers rate each loan. We therefore define two measures of the riskiness of a loan,

based on the ratings given by the loan officers who evaluated loans under the baseline, low-

powered incentive scheme. The first measure is simply the mean risk rating. The second is the

coefficient of variation of the risk rating, which measures the degree of ex-ante disagreement

about the quality of a loan.

In the regressions in Table VII, we restrict the sample to loans which a loan officer ap-

proved; thus the coefficients give the average risk rating of loans approved under a particular

incentive scheme. We find that high-powered incentives lead to more conservative lending,

though this result is significant only for the measure of business and financial risk (Columns

(5) and (6)). We also find that high-powered incentives cause loan officers to shy away from

loans that are risky in the sense that there is greater ex-ante disagreement about the inter-

pretation of information contained in the loan file, as reflected in greater variance of a loan’s

baseline risk rating. Loans approved under high-powered incentives are characterized by a

significantly lower coefficient of variation of their baseline rating.

22

5.4 Lending Decisions and Loan-Level Profit

In Table VIII, we turn to the impact of performance pay on lending decisions and loan

level profit. Loan officers facing origination and repayment bonuses, which do not penal-

ize defaulting loans, are dramatically more likely to approve loan applications, on average

(Columns (1) and (2)). The shift from the baseline condition to high-powered incentives

leads to only slightly more conservative lending decisions, with the share of loans approved

dropping 3.6 and .04%, significant at the 10% level for the specification without loan or

loan officer fixed effects. This is a small effect relative to the mean acceptance rate of 71%

under the baseline. Incentive schemes that reward origination, on the other hand, result in

a dramatic increase in the probability of approval. Under the origination bonus treatment,

loan approvals increase by approximately 8 percentage points, statistically significant at the

1% level. The probability of approval increases monotonically for the two repayment bonus

incentives, with the probability of approval increasing by 9–13.5 and 12.2–15.4 percentage

points, respectively.

Of course, incentivizing more or less lending is relatively easy; the more interesting ques-

tion is whether incentives can make loan officers more discerning. Table VIII, Columns (3)

and (4) show that laxer incentives increase the fraction of good loan clients who are approved,

roughly in proportion to the overall effect on lending. We find a dramatically different pat-

tern for non-performing loans: loan officers facing the high-powered incentive scheme are 11

percentage points less likely to approve these bad loans, a result that is significant at the

five percent level in column (5), despite the smaller sample size. In contrast, we find large

increases in the fraction of non-performing loans approved under an incentive scheme that

does not penalize poor screening decisions. The pattern is similar for the sample of loans that

were initially rejected by the bank, though the statistical significance of the high-powered

incentive effect is lost.

23

In Table VIII, Columns (9) to (12), we study the effect of performance pay on the

profitability of bank lending. Our first measure is the net present value to the lender of

repayments, less the amount disbursed, restricting the sample to loans approved by our

experimental subjects.20 This measure is relevant for a lending institution that seeks to

maximize average profitability per loan made, such as a capital-constrained lender. Columns

(9) and (10) show that high-powered incentives dramatically improve the profitability of

lending, raising profit per loan by US$ 149 to US$ 176 per loan, approximately 5% of the

median loan size. In the final two columns, we consider profit per screened loan, setting the

NPV of a loan that is rejected by an experimental subject to zero. This measure makes most

sense for a lender whose lending opportunities may be limited, perhaps because they face

difficulty sourcing additional clients. Again, we find that high-powered incentives improve

profitability by roughly similar magnitudes, though the result is only statistically significant

in the specification with loan officer fixed-effects.

In our setting, the net interest margin is quite high (around 30%), so one might be con-

cerned that high-powered incentives lead loan officers to behave too conservatively, declining

profitable loans. In fact, we observe that high-powered incentives improve the quality of orig-

ination, and are therefore likely a profitable proposition from the bank’s perspective, even

when screening costs, reduced volume, and the cost of the incentive payments themselves,

are taken into consideration.

5.5 Deferred Compensation

Efforts to regulate the compensation of loan originators have often focused on the alleged

“short-termism” present in many performance contracts in banking and have therefore aimed

at extending the time-horizon of the incentive payments. If loan officers have higher discount20Because we do not observe the outcome of loans files that were originally rejected by the lender, we do

not include these loans in our profit calculations.

24

rates than shareholders, however, deferred compensation will blunt the effect of incentives.21

In this subsection, we test how the effects of incentive payments vary when the time

horizon of payouts is changed. It is worth noting that any compensation that varies with

loan repayment must be paid with some delay, as it takes time to observe whether loans

perform or not. The intent of our experimental treatments is to vary the extent of this

delay in performance based compensation. We are primarily interested in understanding

whether deferred compensation weakens incentives for costly screening effort. We therefore

restrict attention to the subset of “costly information” treatments, in which loan officers

pay to access additional sections of the loan application. We operationalize the concept

of deferred compensation by comparing loan officer behavior under immediate performance

pay (for low-powered, high-powered and origination incentives) to behavior under a series of

treatments, in which incentive payments were awarded after a period of 90 days.22

Table IX presents the results of the deferred compensation intervention. In Panel A, we

report the effect of deferred compensation on screening effort. Panel B reports on the effect

of deferred compensation on risk-taking, and treatment effects of deferred compensation

on loan-level profits are reported in Panel C. Note that in contrast to the previous tables,

the omitted category and relevant basis for comparison here is the low-powered treatment

with costly information. At the foot of the table, we report t-tests comparing the effect of

immediate versus deferred compensation. Consistent with the predictions of our model, the

results show that deferred compensation significantly weakens the impact of high-powered

incentives. This is most apparent in the effect of deferred incentives on screening effort, as

measured by loan sections purchased (Table IX, Columns (3) and (4)). In Column (3), the

difference between immediate high-powered incentive payments and the exact same payments

deferred 90 days is large, [1.225 - (-.454)], and significant at the 1 percent level. While21One need not assume loan officers are impatient: credit-constraints or concern about separation from

employers could also cause loan officers to discount future payments at high rates.22Loan officers were given the option of collecting cash payments or receiving a check by mail.

25

high-powered incentives drive loan officers to lend more conservatively (Columns (5) and

(6)), deferring those same payments attenuates this effect. High-powered incentives lead

loan officers to shy away from loans that appear riskier ex-ante, irrespective of whether the

high-powered incentives are deferred (Columns (7) and (8)). Finally, the point estimates of

profitability are lower for deferred weak (baseline) incentives, as well as the high-powered

incentives, though the difference is significant (at the 10% level) only for weak incentives.

5.6 Relaxing Limited Liability

Just as banks benefiting from deposit insurance and other implicit guarantees may be

tempted to take high-risk, low-NPV gambles, so too might front-line loan officers seeking

to maximize their variable compensation. To test how the presence of limited liability, an

inherent characteristic of virtually all incentive contracts for loan originators, affects loan

officer behavior, we randomly assigned loan officers to a treatment in which participants re-

ceived an initial endowment of Rs 200 (US$ 4.5) at the beginning of each session, which was

theirs to take home unless their incentive payments for the session were negative, in which

case the amount of penalties would be deducted from the endowment. The worst outcome

possible for a loan officer would be to approve two bad loans and decline four good loans

under the high-powered incentive, in which case incentive payments would be Rs -200. The

endowment therefore completely relaxed the limited liability constraint for the session.

Table X presents the results. We find evidence to suggest that relaxing limited liability

indeed increases loan officers’ screening effort (Columns (3) and (4)), though the differences

are not statistically significant. Surprisingly, loan officers approve loans that appear to be

on average lower quality (Column 5) when limited liability is relaxed. When taking lending

decisions, loan officers are more conservative without limited liability, though the size of this

difference is modest (the difference in coefficients in Column (7) is 2.9 percentage points) and

26

not statistically significant. Interestingly, we are unable to detect any systematic difference

in the profitability of lending to banks under either scheme. Taken together, these results

suggest that ensuring loan officers have more skin in the game has at most modest effects

on effort and lending decisions.

5.7 Do Loan Officer Characteristics Matter?

The analysis thus far shows that the structure of performance incentives has important effects

on loan officer behavior. However, individual ability, experience, and other characteristics

may play an important role in determining how loan officers make lending decisions, as well

as how they respond to incentives. In any real-world setting, without random assignment,

it would be difficult to tease apart the relationship between individual characteristics and

performance: for example, higher-ranking individuals may have more advisory support, or be

assigned to make decisions on loans that are more risky or informationally opaque. A virtue

of our experiment is that we observe loan officers with a variety of demographic and personal

characteristics perform an identical task under identical, exogenously assigned conditions.

Previous literature has used various measures to identify determinants of success, often

in entrepreneurial activities. Among the traits and dispositions that repeatedly appear as

good predictors are biological determinants, such as IQ, gender, and age (see, for example,

Djankov et al. (2007), Landier and Thesmar (2009)). Related literature documents cultural

predictors, such as occupation of the parent and ethnic ties. The psychology literature has

also identified broad personality factors associated with entrepreneurial start-up and success,

such as risk taking, neuroticism and the need to be motivated to achieve (de Mel, McKenzie

and Woodruff (2009), Zhao and Seibert (2006)). Although there is much reason to believe

that, similar to many other tasks that require a tradeoff between risk and return, patterns of

loan officer decision making are likely to be influenced by personal characteristics, we are not

27

aware of any prior evidence on what personality characteristics predict success in lending.23

In this section, we explore these open empirical question by considering, first, the effect of

personal attributes on loan officer decision making and, second, heterogeneity in the response

to performance incentives among loan officers with different demographic characteristics.

5.7.1 Loan Officer Characteristics and Lending Decisions

Before we explore heterogeneity in the response to incentives, Table XI examines how lending

decisions vary with loan officer characteristics. To do this, we augment equation 1 with a

demographic characteristic, such as age or gender, or the measurement of a personality

trait. We omit other loan officer characteristics from the regression, but continue to include

treatment dummies, as well as week and randomization stratum fixed effects.

The results are intriguing and demonstrate that demographic characteristic and person-

ality traits significantly affect loan officer behavior. A loan officer’s age has a small, but

perceptible effect on internal ratings and loan approvals: a loan officer ten years older rates

a file, on average, .06 standard deviations higher quality, and is one percentage point more

likely to approve a given loan application. The largest effects appear in loan officer effort.

Loan officers with the highest rank (5/5) spend, on average, Rs 12 more per session (from a

mean of 24) on reviewing costly information than loan officers at the lowest rank (1/5).

A large literature examines the performance of private versus government-owned banks

(see, for example, LaPorta, Lopez-De-Silanes and Shleifer (2002) and Cole (2009)). An

unanswered question in that literature is whether incentives alone can explain differences

in performance, or whether the type of person working in a public sector undertaking may

behave differently. Table XI shows that employees of private sector banks work harder

(are willing to pay a higher cost to observe information), and rate the same loans higher.

Importantly, they also seem to make better decisions: they are more likely to accept good23For a comprehensive review of the related psychology literature, see Frese and Rauch (2007).

28

loans, no more likely to accept bad loans, and their decisions are on average $75 (or 2.2%

percent of the median loan size) more profitable than the decisions of public sector bankers.

We next examine whether lending behavior can be predicted by standard measures of

personality traits. Following the experiment, we asked 53 loan officers to complete a standard

personality test (John, Donahue and Kentle (1991)).24 While these measures have been

validated and are widely used in experimental economics, to the best of our knowledge this is

the first application in finance. We find that personality matters: agreeable and conscientious

individuals spend significantly more on costly information, while neurotic individuals shirk.

Personality also affects ratings, and the ability to correctly identify good loans. These

effects are economically meaningful: a loan officer at the 75th percentile of the agreeability

distribution will approve 4.3 percentage points more good loans than an individual at the

25th percentile of the agreeableness distribution. Two of the five personality measures predict

profitability: extroverts make better decisions, while more neurotic individuals turn down so

many good loans that their average profitability is lower.

Finally, we analyze the effect of risk aversion and patience (more patient individuals

have lower discount rates) on behavior. We find that, on average, risk averse individuals

spend less on costly information, but are more likely to approve both good and bad loans,

though the latter effects are small (the inter quartile range for risk aversion on accepting

good applications is 1.2 percentage points).

In summary, loan officer identity seems to matter. Different individuals, facing the iden-

tical information set and identical incentives, behave quite differently, with important conse-

quences for lending. In the final section, we turn to the possibility that the incentive schemes

themselves have heterogeneous effects.24Summary statistics of these characteristics are given in Table II, Panel B.

29

5.7.2 Loan Officer Characteristics and the Response to Incentives

Does the response of credit officers to incentives vary with individual characteristics, such

as age or experience? Table XII reports regressions which augment equation (1) with a loan

officer characteristic, and interact that characteristic with treatment dummies. Because

there are many more interactions possible than the scope of this paper allows us to report,

we focus on what we believe are the most salient characteristics.25

Panel A examines whether the effect of incentives varies with the age of the loan officer.26

The odd columns present the main effect of age and the incentive schemes, while the even

columns report the interaction between age and incentives. We find that older officers rate

loans higher, but that their ratings are less responsive to incentives: particularly, when faced

with an origination bonus, a 50 year old loan officer increases average ratings 40% less than

a 30 year-old loan officer.

Turning to rank, we find that the lowest ranking loan officers barely reduce expenditures

on information when faced with an origination bonus scheme, but higher ranked loan officers

decrease their expenditures substantially. Perhaps the most significant difference in response

to incentives occur by gender. Men rate loans higher, but women inflate ratings significantly

more when facing origination bonuses. Men also accept a significantly higher fraction of loans

under low-powered incentives (.06). While women make more lenient lending decisions under

high-powered incentives, men become stricter. Finally, women respond to the origination

bonus by increasing their acceptance rates dramatically more than men. While the previous

section showed private sector bankers behave dramatically differently, Panel D suggests that

they respond in generally similar ways to incentives. One exception is in Column (2): private

bankers act more like homo oeconomicus, reducing expenditure of information much more

than public sector bankers when faced with origination bonuses. Finally, we note that, at25Additional interaction results are reported in the working paper version.26We divide age by 10 to avoid very small coefficients.

30

least if evaluated by their willingness to pay a cost to obtain more information, agreeable

loan officers make better employees. They also shirk approximately one-fourth less when

facing the origination incentive.

Risk aversion and time preferences (not reported) do not have dramatic impact on the

efficacy of incentive schemes (results available in working paper). The effect of incentives

appears generally to be weaker on individuals with lower discount rates. However, we do not

find that risk-averse individuals become particularly conservative in lending decisions when

faced with the high-powered incentive scheme.

In short, we find some evidence of the heterogeneous effect of incentives, though we are

not persuaded that the weight of the evidence is strong enough to recommend that banks

provide different incentive schemes for different types of people. In contrast, the first-order

effects of individual characteristics on lending behavior are quite meaningful. Incentive

contracts may be important in helping banks attract workers whose personal characteristics

are compatible with profitable lending.

6 Discussion and Conclusion

Recent research has presented convincing evidence that incentives rewarding loan origination

may cause severe agency problems and increase credit risk, either by inducing lax screening

standards Agarwal and Ben-David (2012), or by tempting loan officers to game approval

cutoffs even when such cutoffs are based on hard information (Berg, Puri and Rocholl, 2012).

Yet, to date there has been no evidence on whether performance-based compensation can

remedy these problems. The literature is similarly silent on the degree to which contracting

constraints inherent to the structure of performance incentives in lending, such as deferred

compensation and limited liability, affect how loan officers respond to pay-for-performance.

In this paper, we analyze the underwriting process of small-business loans in an emerging

31

market, using a series of experiments with experienced loan officers from Indian commercial

banks. The loan files evaluated in these experiments consist of the loan applications of

entrepreneurs seeking their first commercial loan, which requires extensive screening effort

and is therefore particularly sensitive to loan officer judgment.

We provide the first rigorous test of theories of loan officer decision-making, using evidence

from a series of randomized experiments. Because our experimental design allows us to

capture normally unobservable aspects of loan officer behavior, such as effort spent in the

evaluation of borrower information, we directly measure the relationship between incentives,

effort, and performance. We additionally observe loan officers’ subjective risk-assessment,

which allows us to trace the impact of incentives on the perception of credit risk.

Comparing three commonly implemented classes of incentive schemes, we find a strong

and economically significant impact of monetary incentives on screening effort, risk-assessment,

and the profitability of originated loans. High-powered incentives that penalize the origina-

tion of non-performing loans while rewarding profitable lending decisions cause loan officers

to exert greater screening effort, approve fewer loans and increase the profits per originated

loan. In line with the predictions of a simple model of incentives and loan officer decision-

making, these effects are attenuated when deferred compensation is introduced. Interestingly,

we find that incentives affect not only actual risk-taking, but also loan officers’ subjective

perception of credit risk: more permissive incentive schemes lead loan officers to rate loans

as significantly less risky than the same loans evaluated under pay-for-performance.

While we acknowledge that there are limitations to our set up as compared to a pure field

experiment, the data in this study represent the allocation of approximately US$ 88 million in

credit, something that would be difficult to achieve in a field experiment. The combination of

a lab and field experiment enables us to ask loan officers with very different backgrounds and

skills to evaluate exactly the same loans under exogenously assigned incentives and allows

us to measure aspects of loan officer behavior that would otherwise be unobservable, such

32

as screening effort in the evaluation of borrower information and the subjective assessment

of credit risk. The experiments in this paper thus represent the first step of an ambitious

agenda to fully understand the loan underwriting process.

Lenders have increasingly relied on credit scoring models rather than human judgment.

But it is unclear whether credit scoring models can outperform human judgment, particularly

in informationally opaque credit markets, such as the one we study. Nor is it obvious what

individual characteristics are associated with screening ability and to what extent they help

or hinder the use of performance incentives as a tool to manage credit-risk in commercial

lending. The results in this paper provide a first step in answering these important questions.

33

Appendix

A Simple Model of Loan Officer Decision Making

To guide the analysis, we describe a simple model that highlights the key frictions that

may prevent the implementation of the optimal contract, and describes how changes in loan

officer incentives affect screening behavior and lending decisions.

Agents. The model encompasses firms, loan officers, and the bank. The bank is risk-

neutral, while loan officers are risk-averse with uw > 0 and uww < 0. Firms seek to borrow

1 unit of capital from the bank. They invest in a project which either succeeds, generating

income, or fails, leaving zero residual value. There are two types of firms: good firms of type

θG with probability of investment success p, and bad firms of type θB, with probability of

investment success 0. The ex-ante fraction of good firms is π. We assume that the bank has

a net cost of capital normalized to 0, and charges interest rate r > 0. If the bank makes a

loan that is repaid, it therefore earns net interest margin r. If the loan defaults, the bank

loses 1 unit of capital. If the bank were to lend 1 unit of capital to all applicants, a loan

would be repaid with probability πp and earning expected return πp(1 + r)− 1. We assume

this amount to be negative, so that it is not profitable for the bank to lend to all applicants.

Information and Screening. While a firm’s type is not observed, a loan officer may screen

a loan application in an attempt to determine the firm’s type. This requires effort, which

comes at private cost e > 0 to the loan officer. We additionally allow for the possibility that

an intrinsically motivated loan officer derives non-pecuniary utility m ≥ 0 from screening,

and assume that both e and m are specific to an individual loan officer and independent

of monetary incentives. If a loan officer engages in screening, she observes either a fully

informative bad news signal, σB, indicating that the firm is type θB, and will default with

certainty , or the “no bad news” signal σG. Bad firms generate a bad signal with probability

34

γ, and a good signal with probability 1-γ. Good firms generate a good signal with certainty.

Hence, the probability of observing a bad signal conditional on firm type is

P (σB) ={γ if borrower is type θB

0 if borrower is type θG

It follows that the posterior probability of a firm being bad after receiving a bad signal is

P(θB|σB) = 1, and the probability of the firm being good after observing a good signal is

P (θG|σG) = ππ+(1−γ)(1−π) . We assume that it is profitable to lend to a firm with a good

signal, even when screening costs are taken into consideration, so that

π {pr + (1− p)(−1)}+ (1− π) {γ · 0 + (1− γ)(−1)} ≥ e−m (A.2)

Contracts. The bank may offer the loan officer a contract w = [w,wD, w] to induce screening

effort. The contract specifies a payment w for declining a loan application, and contingent

payments for approving a loan that subsequently performs wP and for approving a loan that

subsequently defaults, wD, where wP , w ∈ [0, r] and wD ∈ [−1, 0]. The bank’s problem is to

choose w = [wP , wD, w] to maximize profitability. The bank does not observe the outcome

of a loan that is screened out by the loan officer.

Expected Utility. Loan officers choose the return to three possible actions: declining a loan

without screening, approving the loan without screening, or screening the loan application

and approving the loan only if no bad signal is observed. We consider the outcome of each

action in turn. If a loan officer rejects a loan without screening, her expected utility is simply

uR = u(w). If the loan officer approves a loan without screening, her expected utility is

uNS = πpu(wP ) + (1− πp)u(wD) (A.3)

35

If an officer screens and approves only when no negative signal is observed, her utility is27

uS = πpu(wP ) + {1− πp− γ(1− π)}u(wD) + {(1− π) γ} u(w)− e+m (A.4)

Incentive Compatibility. We begin by remarking that, in the case of a risk-neutral loan

officer with unlimited wealth, the efficient outcome can be obtained by setting w = [r,−1, 0],

effectively selling the loan to the loan officer and making her the residual claimant. However,

in practice this contract is expensive for the bank (as it gives the entire profit from the loan

to the loan officer) and not feasible in practice, as the loan officer would be liable for the

total amount of the loan in case of default. Hence, if the bank is to motivate the loan officer

to exert screening effort, it needs to offer a contract that satisfies two incentive constraints:

uS ≥ uNS and uS ≥ uR. The first constraint requires that the returns to effort be greater

than the cost of effort. This condition simplifies to:

γ {u(w)− u(wD)} (1− π) +m ≥ e (A.5)

The second constraint requires that the loan officer prefer screening to declining all loans:

πpu(wP ) + {1− πp+ γ(π − 1)}u(wD)− {1 + γ(π − 1)}u(w) +m ≥ e (A.6)

In practice, since both constraints are upper bounds for the cost of effort, only one will

bind. Nevertheless, the fact that wD and w slacken one constraint while tightening the

other suggests it may be difficult to obtain the optimal incentive scheme, and indeed the

parameter space admits ranges such that profitable lending is not possible. No matter which

constraint binds, it is always weakly easier to induce effort when the cost of effort is lower,27From these conditions, we can also derive the profit of the bank in each case. If a loan officer rejects a loan

without screening, the bank’s profit is ΠR = −w. If the loan officer approves a loan without screening, thebank’s profit is ΠNS = πp(r−wP )−(1−πp)(1+wD), and if the loan officer screens and approves a loan only ifno bad signal is observed, expected profit is ΠS = πp(r−wP )−[π(1−p)+(1−π)(1−γ)](1+wD)−[(1−π)γ]w.

36

the penalty for making a non-performing loan increases, and the outside option of declining

a loan decreases. The effect of increasing wP depends on which incentive compatibility

constraint binds. Loan officers can always be induced to lend, although not necessarily in a

manner that is profitable for the bank.

In the experiment, we focus on the following testable predictions that characterize incen-

tive schemes commonly employed in commercial lending. Taken literally, the model predicts

that loan officers will either screen all loans, or not screen any loans. However, a simple

extension in which e varies by loan, in a way that is observable only to the loan officer,

would generate non-corner solution in screening effort, and the following comparative statics

with respect to the average effort level exerted by a loan officer.

Proposition 1 (Incentive power) ∂e∂wD

and ∂e∂wD

< 0 and ∂e∂wP

> 0. An origination piece rate,as often employed in commercial lending, leads to low screening effort, indiscriminate lend-ing and high default rates. By contrast, high-powered incentives that reward the originationof performing loans while penalizing the approval of bad loans lead to greater effort, moreconservative lending and lower defaults.

Proposition 2 (Deferred compensation) Let δ ∈ (0, 1) denote the time discount rate ofloan officer i. Then δu < u ∀ δ. Deferred compensation weakens the incentive power of anyperformance based contract for a loan officer with a positive discount rate, as the cost ofeffort is not discounted while monetary rewards are.

Proposition 3 (Limited liability) Because ∂e∂wD

and ∂e∂wD

< 0, increasing a loan officer’sliability for non-performing loans from wD ≥ 0 to wD ∈ (−r, 0) leads to greater screeningeffort. More generally, relaxing the limited liability constraint increases the incentive powerof any performance based contract.

Proposition 4 (Intrinsic motivation). ∂e∂m

and ∂e∂m

> 0. The smaller the utility cost ofscrutinizing and the stronger the loan officer’s intrinsic motivation the greater will be theofficer’s observed screening effort, irrespective of the incentive power of the contract.

37

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Figures and Tables

Figure 1: Loan Officer Performance0

12

34

5D

ensi

ty

.3 .4 .5 .6 .7 .8

% of Lending Decisions Correct [Baseline]

Notes: This figure shows the distribution of loan officer performance, measured by the average percentage

of correct decisions per session under the Baseline treatment. The line plots the Kernel density of the

performance distribution. We define a correct lending decision as approving an ex-post performing loan or

declining an ex-post non-performing loan.

44

Figure 2: Learning During the Experiment

(a) Accuracy of Lending Decisions

.4.5

.6.7

.8%

Len

ding

Dec

isio

ns C

orre

ct

0 5 10 15 20

Number of Experimental Sessions Completed

(b) Profitability of Lending Decisions

−.2

0.2

.4.6

Pro

fit p

er A

ppro

ved

Loan

[U

S$

’000

]

0 5 10 15 20

Number of Experimental Sessions Completed

Notes: This figure examines the presence of learning effects over the course of the experiment by plotting (a)

the percentage of correct decisions by the total number of experimental sessions completed and (b) the profit per

approved loan by the number of experimental sessions completed. A correct lending decision is defined as a loan

officer correctly approving a performing loan or correctly declining a loan that became delinquent. The dashed lines

and shaded areas are Kernel-weighted local polynomial regressions with corresponding 95% confidence intervals.

45

Figure 3: Distribution of Internal Ratings

(a) Non-performing Loans

0.0

1.0

2.0

3.0

4

20 30 40 50 60 70 80 90 100

(b) Performing Loans

0.0

1.0

2.0

3.0

4

20 30 40 50 60 70 80 90 100

Notes: This figure plots the distribution of internal ratings assigned to loans evaluated under the baseline treatment.

Panel (a) shows the distribution of risk-ratings for the sample of non-performing loans and loans that were declined

by the Lender ex-ante; panel (b) plots the distribution for performing loans. Vertical lines show the median of the

distribution. A Kolgomorov-Smirnov test rejects equality of distributions at 1% (p-value<0.001).

46

TableI:Su

mmaryof

IncentiveTr

eatm

ents

The

tablesummarizes

theexpe

rimentalincentive

schemes.Eachincentiveschemeconsists

ofacond

itiona

lpaymentw

Pforap

provinga

loan

that

performs,acond

itiona

lpaymentw

Dforap

provingaloan

that

subseque

ntly

defaults

andan

outsidepa

yment

wfordeclininga

loan

,inwhich

case

theou

tcom

eof

theloan

isno

tob

served.Allincentives

referto

cond

itiona

lpayoff

sforan

individu

allend

ingdecision

.

Incentive

Incentive

NCostly

Deferred

Lim

ited

Treatment

Payments

Inform

ation

Com

pen

sation

Liability

[Per

form

|D

efau

lt|

Rej

ect]

No

Yes

No

Yes

No

Yes

ALo

w-Pow

ered

[Baseline]

[20,

0,10]

7,420

3,782

3,638

6,568

852

N/A

7,420

BHigh-Pow

ered

[50,

-100,0

]2,946

654

2,292

2,496

450

978

1,968

COrigina

tion

Bon

us[20,

20,0

]2,548

762

1,786

1,632

916

N/A

2,548

47

Table II: Loan Officer Summary StatisticsPanel A reports demographic summary statistics of the participants (Columns (1) to (4)), comparing experiment par-ticipants to the staff of all loan officers of a large public sector bank in the state in which the experiment was carriedout (Columns (5) to (8)). Rank is the loan officer’s seniority level in the bank ranging from 1 (lowest) to 5 (highest).Experience is the total number of years the participant has been employed with the bank. Branch Manager is a dummyvariable indicating whether the participant has ever served as a branch manager or in a comparable management role.Business Experience is a dummy variable taking on a value of 1 if a loan officer reports having any previous businessexperience outside banking. Panel B shows summary statistics for the subsample of loan officers that completed thepersonality test.

Panel A

Experiment participants [N=209] Bank sample [N=3,111]

N Mean Median StdDev N Mean Median StdDev

(1) (2) (3) (4) (5) (6) (7) (8)

Male 206 0.90 1.00 0.30 3,111 0.9 1.00 0.30

Age 206 37.60 35.00 10.94 3,111 37.9 35.00 12.0

Education [Master’s degree] 200 0.33 0.00 0.47 N/A N/A N/A N/A

Experience [Years] 206 12.76 10.00 11.30 3,111 13.90 11.00 13.00

Rank [1 (Lowest) - 5 (Highest)] 206 1.94 2.00 1.00 3,111 1.60 2.00 0.75

Branch Manager Experience 206 0.33 0.00 0.47 N/A N/A N/A N/A

Business Experience Indicator 206 .47 0.00 .50 N/A N/A N/A N/A

Private Sector Banker 206 0.20 0.00 0.40

Panel B

Experiment participants

with personality test [N=53]

N Mean Median StdDev

(1) (2) (3) (4)

BFI Extroversion 50 3.46 3.38 0.53

BFI Agreeableness 50 3.90 3.89 0.51

BFI Conscientiousness 50 3.92 3.94 0.62

BFI Neuroticism 50 2.64 2.69 0.66

BFI Openness 50 3.60 3.55 0.51

48

TableIII:Lo

anFile

SummaryStatist

ics

Thistablerepo

rtssummarystatistics

forthesampleof

loan

sused

intheexpe

riment.

Colum

ns(4)to

(6)repo

rtsummarystatistics

forthesub-sampleof

performing

loan

san

dcolumns

(7)to

(9)show

summarystatistics

forthesub-sampleof

non-pe

rformingloan

san

dloan

sthat

weredeclined

bytheLe

nder.In

Colum

ns(10)

and(11)

weshow

diffe

rences

inmeans

betw

eenthetw

ogrou

psan

dp-values

from

atest

ofequa

lity.

Mon

thly

revenu

einclud

esbu

siness

revenu

ean

dothersourcesof

household

income.

Persona

lExp

ensesmeasure

aclient’s

mon

thly

person

alexpe

nses

andBusinessExp

ensesmeasure

aclient’s

totalmon

thly

requ

ired

cash

expe

nses,includ

ingall

inpu

tsto

prod

uction

.Mon

thly

DebtServiceisthesum

ofallm

onthly

installm

ents

ontheap

plican

t’sou

tstand

ingloan

s,no

tinclud

ingtheprop

osed

loan

.Allvariab

lesare

deno

minated

inUS$

.*p<

0.10

**p<

0.05

***p<

0.01.

Pan

elA:Entiresample

Pan

elB:Perform

ingloan

sPan

elC:Non

-perf&

declined

Differen

cein

means

[N=67

6][N

=592]

[N=84]

(B)-(C

)

Mean

Median

StDev

Mean

Med

ian

StDev

Mean

Median

StDev

Difference

p>

|t|

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Loancharacteristics

Loan

amou

nt6,00

96,383

2,627

5,987

6,383

2,613

6,147

6,383

2,722

-160

(0.58)

Mon

thly

installm

ent

420

208

855

413

208

878

476

205

620

-63

(0.58)

Loan

tenu

re32

.64

369.04

31.8

367.57

37.9

3614.35

-6.10***

(0.00)

Businessincome

Mon

thly

revenu

e11

,680

6,38

318,621

12,126

6,383

19,257

7,850

5,309

11,224

4,276*

(0.07)

Mon

thly

business

expe

nses

9,81

85,19

117

,438

10,529

5,559

18,354

5,368

3,514

8,771

5,161***

(0.01)

Mon

thly

EBIT

1,84

41,00

76,523

1,904

991

7,002

1,467

1,074

1,388

437

(0.55)

Deb

t

Totald

ebt

6,776

031,572

6,820

033,425

6,504

955

15,887

316

(0.93)

Mon

thly

debt

service

227

0733

226

0777

234

112

358

-8.00

(0.92)

Persona

l

Age

ofbu

siness

11.27

97.99

11.64

98.35

9.5

85.8

2.14**

(0.02)

Mon

thly

person

alexpe

nses

283

223

304

285

223

317

270

231

209

15(0.66)

Creditrepo

rt,a

ccts

overdu

e0.2

00.4

0.18

00.38

0.32

00.47

-0.14**

(0.04)

49

TableIV

:LoanEv

alua

tionSu

mmaryStatist

ics

Thistablerepo

rtssummarystatistics

onlend

ingdecision

sin

theexpe

rimentby

incentivetreatm

ent.

The

tabledisplays

uncond

itiona

lmeans

andstan

dard

deviations.

Colum

ns(2)an

d(3)repo

rtsummarystatistics

forscreeningeff

ortmeasuredas

thenu

mbe

rof

loan

filesections

review

edan

dthenu

mbe

rof

inform

ationcreditsspent

byloan

officers

fortreatm

ents

that

includ

edthe“costlyinform

ation”

cond

ition,

underwhich

participan

tswerechargedto

access

addition

alinform

ation.

Colum

n(4)

repo

rtstheinternal

rating

(normalized

toha

vemeanzero

andstan

dard

deviation1)

assign

edto

loan

sevalua

tedun

dereach

treatm

entcond

ition,

andColum

n(6)repo

rts

profi

tpe

rap

proved

loan

byincentivetreatm

entin

unitsof

US$

’000.Colum

ns(7)to

(10)

repo

rtthepe

rcentage

ofcorrectlend

ingdecision

sby

incentivetreatm

ent.

A“correct”decision

isdefin

edas

approvingaloan

that

ex-postpe

rforms,

orrejectingaloan

that

ex-postdo

esno

tpe

rform,o

rwhich

theba

nkrejected.

NEffort

Riskrating

App

roved

Profit

Evaluations

Correct

Sections

Amou

ntspent

%US$

’000

Sample

Perform

ing

Non

-Declin

ed

review

edon

inform

ation

performing

byba

nk

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Entiresample

14,675

5.06

4.17

0.07

0.75

553.89

0.65

0.80

0.30

0.45

(2.44)

(4.41)

(1.01)

(0.44)

(1882.85)

(0.48)

(0.40)

(0.46)

(0.50)

Baseline

8,39

85.25

4.80

0.00

0.72

527.84

0.65

0.78

0.32

0.52

(2.35)

(4.57)

(1.00)

(0.45)

(1859.50)

(0.48)

(0.42)

(0.47)

(0.50)

High-po

wered

1,96

85.01

4.78

0.23

0.69

576.10

0.65

0.75

0.42

0.48

(2.33)

(4.66)

(1.01)

(0.46)

(1732.70)

(0.48)

(0.43)

(0.49)

(0.50)

Origina

tion

2,54

84.60

3.59

0.13

0.84

554.83

0.66

0.87

0.21

0.29

(2.18)

(3.84)

(1.02)

(0.37)

(1986.07)

(0.48)

(0.33)

(0.41)

(0.45)

50

Table V: The Effect of Incentives on EffortThis table reports treatment effects of performance pay on screening effort. Each column reportsresults from a separate regression. The omitted treatment category is the low-powered baselineincentive. The dependent variable in Column (1) and (2) is number of sections of the loan fileviewed; the dependent variable in Columns (3) and (4) is the number of loan file sections reviewedwhen loan officers were required to pay for additional information. The regressions in Columns (1)and (2) include the entire sample, while Columns (3) and (4) limit the data to evaluations to the“costly information setting.” All regressions include a lab fixed effect, randomization stratum andweek fixed effects, as well as dummies to control for treatment conditions not reported in this table.Loan officer controls include age, seniority, rank, education, and indicators for branch manager andbusiness experience. Standard errors, in parentheses, are clustered at the loan officer × session level.* p<0.10 ** p<0.05 *** p<0.01.

Free information Costly information

Loan file Loan file

sections reviewed sections reviewed

(1) (2) (3) (4)

Baseline, omitted

High-powered 0.434* 0.400*** 1.225*** 0.794***

(0.23) (0.14) (0.42) (0.25)

Origination bonus 0.083 0.005 -0.147 -0.156

(0.22) (0.14) (0.40) (0.21)

Performance bonus low -0.059 -0.133 0.550 0.131

(0.29) (0.21) (0.38) (0.22)

Performance bonus high -0.000 0.019 0.175 -0.084

(0.32) (0.24) (0.32) (0.16)

Loan fixed effects No Yes No Yes

Loan officer fixed effects No Yes No Yes

Loan officer controls Yes No Yes No

Number of observations 14,405 14,675 8,520 8,688

R-squared, adjusted 0.232 0.689 0.271 0.725

51

Table VI: The Effect of Incentives on Risk-AssessmentThis table reports the effect of performance pay on loan officers’ subjective assessment of creditrisk. Each column shows results from a separate regression. The omitted treatment categoryis the low-powered baseline incentive. The dependent variable in regressions (1) and (2) is theoverall risk rating, standardized to have mean zero. The dependent variable in Columns (3) and(4) is the normalized sub-rating for all categories that pertain to the personal risk of a potentialapplicant. In Columns (5) and (6) the dependent variable is the normalized sub-rating for allrating categories that pertain to the business, management and financial risk of a loan applicant.All regressions include a lab fixed effect, randomization stratum and week fixed effects, as wellas dummies to control for treatment conditions not reported in this table. Loan officer controlsinclude age, seniority, rank, education, and indicators for branch manager and business experience.Standard errors are clustered at the loan officer × session level. * p<0.10 ** p<0.05 *** p<0.01.

Internal rating

Overall rating Personal and Business and

management risk financial risk

(1) (2) (3) (4) (5) (6)

Baseline, omitted

High-powered 0.029 0.006 0.011 -0.001 0.054 0.02

(0.09) (0.04) (0.09) (0.04) (0.09) (0.04)

Origination bonus 0.144* 0.006 0.130* -0.015 0.156** 0.021

(0.08) (0.04) (0.08) (0.04) (0.08) (0.04)

Loan fixed effects No Yes No Yes No Yes

Loan officer fixed effects No Yes No Yes No Yes

Loan officer controls Yes No Yes No Yes No

Number of observations 14,405 14,675 14,405 14,675 14,405 14,675

R-squared, adjusted 0.151 0.640 0.142 0.644 0.161 0.626

52

TableVII:T

heEff

ectof

Incentives

onRisk

-Tak

ing

Thistablerepo

rtstreatm

enteff

ects

ofpe

rforman

cepa

yon

risk-tak

ing.

Eachcolumnrepo

rtsresultsfrom

asepa

rate

regression

.The

omittedtreatm

entcategory

isthe

low-pow

ered

baselin

eincentive.

The

depe

ndentvariab

lein

Colum

ns(1)throug

h(6)is

ameasure

ofthepe

rceivedqu

ality

oftheloan

:theaverageinternal

rating

ofeach

loan

repo

rted

byallloan

officers

undertheba

selin

etreatm

ent.

Tocapturethedegree

ofex-anteun

certaintyab

outthequ

ality

ofaloan

,Colum

ns(7)to

(12)

repe

atthe

exercise

usingthecoeffi

cientof

variationof

internal

rating

assign

edto

agivenloan

undertheba

selin

etreatm

entas

thedepe

ndentvariab

le.The

internal

rating

isno

rmalized

toha

vemeanzero

andstan

dard

deviationof

1,henceeff

ectsizesarestan

dard

deviations.Allregression

sinclud

ealabfix

edeff

ect,

rand

omizationstratum

andweekfix

edeff

ects,as

wellas

dummiesto

controlfortreatm

entcond

itions

notrepo

rted

inthis

table.

Loan

officercontrols

includ

eage,

seniority

,rank

,education,

andindicators

for

bran

chman

ager

andbu

siness

expe

rience.Stan

dard

errors,inpa

rentheses,

areclusteredat

theloan

officer×

sessionlevel.*p<

0.10

**p<

0.05

***p<

0.01.

Perceived

qualityof

approved

loan

sPerceived

loan

qualityof

approved

loan

s

[Meanrating

]a[Coefficientof

variation]

b

Overallrating

Persona

land

Businessan

dOverallrating

Persona

land

Businessan

d

man

agem

entrisk

finan

cial

risk

man

agem

entrisk

finan

cial

risk

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Baseline,

omitted

High-po

wered

-0.039

-0.046

-0.024

-0.03

-0.058*

-0.065*

-0.015***

-0.015***

-0.018***

-0.018***

-0.013**

-0.013**

(0.03)

(0.03)

(0.03)

(0.03)

(0.03)

(0.03)

(0.01)

(0.01)

(0.01)

(0.01)

(0.01)

(0.01)

Origina

tion

bonu

s0.02

70.02

50.03

10.032

0.025

0.022

-0.008*

-0.007

-0.007

-0.006

-0.010**

-0.009*

(0.03)

(0.03)

(0.03)

(0.03)

(0.03)

(0.03)

(0.00)

(0.00)

(0.00)

(0.00)

(0.00)

(0.00)

Loan

fixed

effects

No

No

No

No

No

No

No

No

No

No

No

No

Loan

officereff

ects

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

Loan

officercontrols

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Observation

s10

,180

10,402

10,180

10,402

10,180

10,402

9,349

9,555

9,349

9,555

9,349

9,555

R-squ

ared,a

djusted

0.08

0.09

60.07

0.087

0.082

0.098

0.06

0.081

0.062

0.084

0.062

0.082

[a]M

eanrating

assign

edto

loan

application

lby

allloa

noffi

cers

evalua

ting

theloan

undertheba

selin

etreatm

ent.

[b]C

oefficientof

variationof

rating

sassigedto

loan

application

lby

allloanoffi

cers

review

ingtheloan

undertheba

selin

etreatm

ent.

53

TableVIII:Incentives,L

ending

Decision

san

dPr

ofit

Thistablerepo

rtstheeff

ectof

performan

cepa

yon

loan

approvalsan

dtheprofi

tabilityof

lend

ing.

Eachcolumnrepo

rtsresultsfrom

asepa

rate

regression

.The

omitted

treatm

entcatego

ryis

thelow-pow

ered

baselin

eincentive.

The

depe

ndentvariab

lein

Colum

ns(1)to

(8)is

adu

mmyequa

lto

oneforloan

sap

proved

byan

expe

rimental

participan

tan

dzero

otherw

ise.

The

estimates

inColum

ns(1)an

d(2)areba

sedon

thefullsample.

Estim

ates

inColum

ns(3)an

d(4)areba

sedon

thesampleof

performing

loan

s,estimates

inColum

ns(5)an

d(6)areba

sedon

thesampleof

non-pe

rformingloan

s,an

destimates

inColum

ns(7)an

d(8)areba

sedon

thesampleof

loan

sthat

were

initially

declined

bytheLe

nder.Colum

ns(9)to

(12)

repo

rttreatm

entestimates

ofincentives

onprofi

tpe

rap

proved

loan

andprofi

tpe

rscreened

loan

,inun

itsof

US$

’000.

Allregression

sinclud

ealabfix

edeff

ect,

rand

omizationstratum

andweekfix

edeff

ects,as

wellas

dummiesto

controlfortreatm

entcond

itions

notrepo

rted

inthis

table.

Loan

officercontrolsinclud

eag

e,seniority

,ran

k,education,

andindicators

forbran

chman

ager

andbu

siness

expe

rience.Stan

dard

errors,inpa

rentheses,areclusteredat

the

loan

officer×

sessionlevel.*p<

0.10

**p<

0.05

***p<

0.01.

Pan

elA:App

roved

Pan

elB:Profit

Total

Perform

ing

Non

-perform

ing

Declin

edby

bank

perap

proved

loan

perscreened

loan

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Baseline,

omitted

High-po

wered

-0.036

*-0.004

-0.010

0.015

-0.110**

-0.063

-0.042

-0.014

148.986*

175.907**

84.900

114.930*

(0.02)

(0.02)

(0.03)

(0.03)

(0.06)

(0.06)

(0.06)

(0.06)

(85.01)

(86.81)

(62.51)

(63.76)

Origina

tion

bonu

s0.08

3***

0.07

9***

0.087***

0.068***

0.048

0.082*

0.098*

0.102*

29.489

-4.182

80.193

56.500

(0.02)

(0.02)

(0.02)

(0.02)

(0.05)

(0.05)

(0.06)

(0.05)

(78.04)

(79.02)

(60.96)

(61.21)

Loan

fixed

effects

No

Yes

No

Yes

No

Yes

No

Yes

No

No

No

No

Loan

officerfix

edeff

ects

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

Loan

officercontrols

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Num

berof

observations

14,405

14,675

9,398

9,575

2,730

2,778

2,277

2,322

9,242

9,435

11,853

12,074

R-squ

ared,a

djusted

0.02

50.212

0.025

0.203

0.054

0.244

0.080

0.300

0.009

0.020

0.007

0.016

54

TableIX

:DeferredCom

pensation

Thistablerepo

rtstreatm

enteff

ects

ofdeferringpe

rforman

cepa

yby

threemon

ths.

Eachcolumnrepo

rtsresultsfrom

asepa

rate

regression

.The

omittedtreatm

entcategory

isthelow-pow

ered

baselin

econd

ition.

The

depe

ndentvariab

lein

Colum

ns(1)an

d(2)thenu

mbe

rof

loan

filesections

review

edforeach

evalua

tedloan

.The

depe

ndentvariab

lein

Colum

ns(3)an

d(4)is

theam

ount

spenton

review

ingad

dition

alinform

ationun

derthe“costlyinform

ation”

cond

ition.

InColum

ns(5)an

d(6)weconsider

theeff

ectof

deferred

compe

nsationon

risk-tak

ing.

The

depe

ndentvariab

leis

themeanan

dcoeffi

cientof

variationof

internal

rating

sassign

edto

each

loan

undertheba

selin

eforloan

sap

proved

bypa

rticipan

tsin

theexpe

rimentas

theou

tcom

eof

interest,w

iththesamplerestricted

toloan

stheloan

officerap

proves.The

depe

ndentvariab

lein

Colum

ns(7)an

d(8)isadu

mmyequa

lto1ifaloan

evalua

tedin

theexpe

rimentwas

approved

and0otherw

ise.

The

depe

ndentvariab

lesin

Colum

ns(9)an

d(10)

repo

rttreatm

entestimates

ofmon

etaryincentives

onprofi

tpe

rap

proved

loan

andprofi

tpe

rscreened

loan

,inun

itsof

US$

’000.Allregression

sinclud

ealabfix

edeff

ect,

rand

omizationstratum

andweek

fixed

effects,a

swella

sdu

mmiesto

controlfor

treatm

entcond

itions

notrepo

rted

inthis

table.

Loan

officercontrols

includ

eage,

seniority

,ran

k,education,

andindicators

for

bran

chman

ager

andbu

siness

expe

rience.Te

ststatistics

atthefoot

ofthetablereferto

t-testsfortheequa

lityof

coeffi

cients

betw

eenthedeferred

andno

n-deferred

treatm

ent

dummies.

Stan

dard

errors,inpa

rentheses,

areclusteredat

theloan

officer×

sessionlevel.*p<

0.10

**p<

0.05

***p<

0.01.

Pan

elA:Sc

reen

ingeff

ort

Pan

elB:Risk-taking

Pan

elC:Len

ding

andprofi

t

Loan

file

Amou

ntspent

AverageInternal

Rating

App

roved

Profit

perloan

sections

review

edon

inform

ation

Mean

cvap

proved

screened

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Baseline,

omitted

Baseline,

deferred

-0.448*

-0.519

***

-0.538

-0.248

0.100***

-0.013***

0.000

0.025

-144.137*

-133.337*

(0.24)

(0.11)

(0.35)

(0.20)

(0.04)

(0.00)

(0.02)

(0.02)

(73.79)

(70.03)

High-po

wered

0.10

2-0.125

1.225***

0.794***

0.081**

-0.011**

-0.048**

-0.060***

39.656

78.682

(0.25)

(0.13)

(0.42)

(0.25)

(0.03)

(0.01)

(0.02)

(0.02)

(71.50)

(65.68)

High-po

wered,d

eferred

-0.512

*-0.364

**-0.454

0.034

0.048

-0.012*

-0.021

-0.017

-60.365

-52.466

(0.28)

(0.15)

(0.50)

(0.29)

(0.04)

(0.01)

(0.03)

(0.03)

(105.13)

(100.10)

Origina

tion

bonu

s-0.409

*-0.497

***

-0.147

-0.156

0.184***

-0.016***

0.110***

0.093***

-165.972**

-65.190

(0.25)

(0.12)

(0.40)

(0.21)

(0.04)

(0.00)

(0.02)

(0.02)

(74.73)

(69.84)

Origina

tion

bonu

s,deferred

-0.319

-0.517

***

-0.207

-0.387*

0.185***

-0.008*

0.079***

0.090***

-55.138

-15.003

(0.24)

(0.12)

(0.37)

(0.23)

(0.04)

(0.00)

(0.02)

(0.02)

(65.11)

(62.48)

Loan

effects

No

Yes

No

Yes

No

No

No

Yes

No

No

Loan

officereff

ects

No

Yes

No

Yes

Yes

Yes

No

Yes

Yes

Yes

Loan

officercontrols

Yes

No

Yes

No

No

No

Yes

No

No

No

Test

:im

med

iate

=de

ferr

ed

Baseline

[0.06]

[0.00]

[0.12]

[0.21]

[0.01]

[0.01]

[1.00]

[0.19]

[0.05]

[0.06]

High-po

wered

[0.01]

[0.09]

[0.00]

[0.02]

[0.38]

[0.90]

[0.33]

[0.10]

[0.35]

[0.19]

Origina

tion

bonu

s[0.56]

[0.81]

[0.88]

[0.28]

[0.97]

[0.07]

[0.08]

[0.88]

[0.10]

[0.44]

Observation

s8,52

08,68

88,520

8,688

8,090

7,263

8,520

8,520

5,619

7,741

R-squ

ared,a

djusted

0.28

20.72

40.271

0.725

0.075

0.104

0.054

0.191

0.654

0.43

55

TableX:R

elax

ingLimite

dLiab

ility

Thistablerepo

rtstheeff

ectof

relaxing

loan

officers’lim

ited

liabilityconstraint.Eachcolumnrepo

rtsresultsfrom

asepa

rate

regression

,the

omittedcategory

ineach

regression

isthelow-pow

ered

baselin

etreatm

ent.

Pan

elA

(Colum

ns(1)to

(4))

repo

rttreatm

enteff

ects

onscreeningeff

ort,

Pan

elB

(colum

ns(5)an

d(6))

repo

rttreatm

enteff

ects

onrisk-tak

ingan

dPan

elC

(Colum

ns(6)to

(8))

repo

rttreatm

enteff

ects

onloan

approvalsan

dprofi

tpe

rap

proved

loan

.The

depe

ndentvariab

lein

Colum

n(7)-(8)is

adu

mmy

equa

lto1ifaloan

evalua

tedin

theexpe

rimentwas

approved

and0otherw

ise.

The

depe

ndentvariab

lein

Colum

ns(9)an

d(10)

aretheba

nk’sprofi

tpe

rap

proved

loan

,and

the

bank

’sprofi

tpe

rscreened

loan

,respe

ctively,

deno

minated

inun

itsof

US$

’000.Allregression

sinclud

ealabfix

edeff

ect,

rand

omizationstratum

andweekfix

edeff

ects,a

swell

asdu

mmiesto

controlfor

treatm

entcond

itions

notrepo

rted

inthis

table.

Loan

officercontrols

includ

eage,

seniority

,ran

k,education,

andindicators

forbran

chman

ager

and

business

expe

rience.Te

ststatistics

atthefoot

ofthetablereferto

t-testsfortheequa

lityof

coeffi

cients

betw

eenthehigh

-pow

ered

treatm

entdu

mmieswhenlim

ited

liabilityis

presentvs.relaxed.

Stan

dard

errors,inpa

rentheses,

areclusteredat

theloan

officer×

sessionlevel.*p<

0.10

**p<

0.05

***p<

0.01.

Pan

elA:Sc

reen

ingeff

ort

Pan

elB:Risk-taking

Pan

elC:Len

ding

andprofi

t

Loan

file

Inform

ation

Internal

rating

[baseline]

App

roved

Profit

perloan

sections

review

edcreditsspent

Mean

cvap

proved

screened

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Baseline,

omitted

[credit]

High-po

wered

0.34

5**

0.24

8**

1.225***

0.794***

0.081**

-0.011**

-0.048**

-0.060***

39.656

78.682

[credit]

(0.16)

(0.10)

(0.42)

(0.25)

(0.03)

(0.01)

(0.02)

(0.02)

(71.50)

(65.68)

High-po

wered

0.55

5***

0.37

2***

1.900***

1.260***

-0.057*

0.006

-0.077***

-0.074***

49.900

22.940

[credit+

endo

wment]

(0.16)

(0.08)

(0.44)

(0.22)

(0.03)

(0.01)

(0.02)

(0.02)

(80.04)

(69.50)

Loan

effects

No

Yes

No

Yes

No

No

No

Yes

No

No

Loan

officereff

ects

No

Yes

No

Yes

Yes

Yes

No

Yes

Yes

Yes

Loan

officercontrols

Yes

No

Yes

No

No

No

Yes

No

No

No

Test

:H

igh-

Pow

ered

noen

dow

men

t=

Hig

h-po

wer

edw

ithen

dow

men

t:[0.34]

[0.34]

[0.25]

[0.17]

[0.00]

[0.00]

[0.33]

[0.63]

[0.92]

[0.53]

Observation

s8,52

08,68

88,520

8,688

6,100

5,463

8,520

8,688

5,694

7,222

R-squ

ared,a

djusted

0.28

20.724

0.271

0.725

0.073

0.076

0.054

0.240

0.661

0.511

56

TableXI:DoLo

anOfficerCha

racterist

icsAffe

ctLe

ndingBe

havior?

Thistablerepo

rtstheresultsfrom

aseries

ofregression

sof

outcom

evariab

les,indicatedat

thetopof

each

column,

onincentivetreatm

entdu

mmies(not

repo

rted)an

dasing

leloan

officercharacteristic,ind

icated

byrows.

Eachcellrepresents

thecoeffi

cientfrom

asepe

rate

regression

.Add

itiona

lcon

trolsinclud

ealabfix

edeff

ect,rand

omizationstratum

andweekfix

ed-effe

cts,

aswella

controlfor

numbe

rof

sessions

completed.Stan

dard

errors

areclusteredat

theloan

officer×

sessionlevel.*p<

0.10

**p<

0.05

***p<

0.01.

Pan

elB:

Pan

elA:Sc

reen

ingeff

ort

Risk-assessment

Pan

elC:Len

ding

andprofi

t

Loan

file

Inform

ation

Internal

Rating

App

roved

Profit

perloan

sections

review

edcreditsspent

Total

Perform

ing

Non

-perf.

Declin

edap

proved

screened

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Loanoffi

cercharacteristics

Loan

officerag

e-0.15*

**-0.10

0.06***

0.01*

0.01***

-0.00

0.00

1.15

7.88

(0.03)

(0.11)

(0.02)

(0.00)

(0.00)

(0.00)

(0.01)

(17.07)

(13.38)

Loan

officerrank

0.18**

*0.43***

-0.02

-0.01*

0.00

-0.00*

-0.01

3.96

-1.03

(0.03)

(0.10)

(0.02)

(0.00)

(0.00)

(0.00)

(0.01)

(15.29)

(11.78)

Loan

officermale

-0.44*

**-0.36

-0.07

0.00

0.00

-0.00

-0.02

-67.27

-57.43

(0.11)

(0.31)

(0.05)

(0.01)

(0.01)

(0.01)

(0.04)

(52.58)

(41.50)

Private

sector

bank

er0.49

***

1.49***

0.28**

0.03***

0.02**

0.01

0.05

70.03

74.50**

(0.09)

(0.29)

(0.04)

(0.01)

(0.01)

(0.01)

(0.03)

(50.62)

(37.96)

BFIpersona

litytest

Extroversion

-0.01

-0.02

0.12**

0.01

0.03***

-0.01

-0.02

108.26**

97.82**

(0.11)

(0.22)

(0.05)

(0.01)

(0.01)

(0.00)

(0.03)

(50.79)

(41.40)

Agreeab

leness

0.24

**1.38***

0.12***

0.01

0.02**

-0.01

-0.10***

35.34

48.11

(0.10)

(0.22)

(0.05)

(0.01)

(0.01)

(0.01)

(0.03)

(51.91)

(40.49)

Con

scientiousness

0.07

0.70***

0.27***

0.01

0.01

-0.00

-0.04

30.96

31.65

(0.09)

(0.22)

(0.04)

(0.01)

(0.01)

(0.00)

(0.03)

(48.96)

(37.33)

Neuroticism

0.06

-0.51***

-0.09**

-0.02***

-0.02***

-0.01

0.03

-44.65

-57.48*

(0.08)

(0.19)

(0.04)

(0.01)

(0.01)

(0.00)

(0.03)

(41.08)

(33.00)

Ope

nness

-0.13

0.25

0.30***

0.00

0.02**

-0.00

-0.11***

36.04

45.43

(0.10)

(0.27)

(0.05)

(0.01)

(0.01)

(0.01)

(0.04)

(57.59)

(45.48)

57

TableXII:H

eterogeneity

intheRespo

nseto

Incentives

Thistablerepo

rtstheeff

ectof

incentiveschemes

onloan

officeractivity,a

ndho

wtheseeff

ects

vary

byloan

officercharacteristics.

Ineach

panel,apa

irof

columns

repo

rtthe

mainan

dhetergenou

seff

ects

(respe

ctively)

ofincentives,b

ythecharacteristic

indicatedin

thepa

nelh

eading

.The

oddcolumns

indicate

maineff

ects

ofthecharacteristic

and

treatm

entcoeffi

cients,w

hile

theeven

columns

indicate

theinteraction.

Stan

dard

errors

areclusteredat

theloan

officer×

sessionlevel.*p<

0.10

**p<

0.05

***p<

0.01.

Screen

ingEffort

Risk-assessment

Len

ding

andprofi

t

Info

creditsspent

Internal

rating

App

roved

Total

Perform

ing

Non

-perform

ing

MainEff

Interaction

MainEff

Interaction

MainEff

Interaction

MainEff

Interaction

MainEff

Interaction

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Pan

elA:by

Age

-0.01

0.10***

0.02**

0.02**

0.01

(0.16)

(0.03)

(0.01)

(0.01)

(0.02)

High

0.49

0.16

0.37

-0.10

-0.02

0.00

-0.04

0.01

0.05

-0.04

(1.29)

(0.37)

(0.25)

(0.07)

(0.06)

(0.02)

(0.08)

(0.02)

(0.19)

(0.05)

Orig

0.17

-0.08

0.53**

-0.11*

0.18***

-0.03

0.17***

-0.02

0.12

-0.02

(1.10)

(0.30)

(0.23)

(0.06)

(0.06)

(0.02)

(0.07)

(0.02)

(0.14)

(0.04)

Pan

elB:by

Gen

der

0.63

***

-0.01

-0.01

0.00

-0.02

[Male=

1](0.16)

(0.03)

(0.01)

(0.01)

(0.02)

High

1.92

*-0.42

0.00

0.01

0.03

-0.03

-0.02

0.01

0.12

-0.10**

(1.01)

(0.47)

(0.22)

(0.09)

(0.04)

(0.02)

(0.05)

(0.02)

(0.12)

(0.05)

Orig

3.08

***

-1.48

-0.03

0.09

0.05

0.02

0.06

0.02

-0.11

0.08*

(0.76)

(0.26)

(0.16)

(0.06)

(0.04)

(0.02)

(0.04)

(0.02)

(0.11)

(0.04)

Pan

elC:by

Ran

k-0.50

0.15

0.06

0.03

0.13

Ran

k[1(low

)-5(high

)](0.52)

(0.09)

(0.02)

(0.03)

(0.06)

High

-1.22

2.43

***

0.34

-0.32

0.13

-0.18

0.10

-0.12

0.11

-0.24

(0.80)

(0.87)

(0.35)

(0.36)

(0.06)

(0.06)

(0.06)

(0.06)

(0.16)

(0.17)

Orig

1.33

-1.75

0.72

-0.60

0.27

-0.20

0.23

-0.16

0.25

-0.21

(1.25)

(1.27)

(0.23)

(0.23)

(0.05)

(0.05)

(0.04)

(0.05)

(0.13)

(0.13)

Pan

elD:by

Employer

1.38

***

0.24***

0.02

0.02

-0.02

[Private

sector

bank

er=1]

(0.41)

(0.07)

(0.02)

(0.02)

(0.04)

High

1.24

*-0.73

0.04

0.03

-0.03

0.01

0.01

-0.02

-0.17*

0.13

(0.66)

(0.80)

(0.13)

(0.16)

(0.03)

(0.04)

(0.04)

(0.05)

(0.09)

(0.11)

Orig

0.64

-1.54*

*0.08

0.16

0.07***

0.04

0.08***

0.03

0.04

0.06

(0.61)

(0.73)

(0.10)

(0.13)

(0.03)

(0.04)

(0.03)

(0.04)

(0.07)

(0.09)

58

Supplementary Appendix

59

A Data Appendix

Table OA.I: Description of Variables

Variable Description

Number of loan file sections

reviewedNumber of loan file sections reviewed out of a total of 10 sections: borrower profile,

application form, documentation list, deviations from financial ratios, income state-

ment balance sheet, site visit report (residence), site visit report (business), trade

reference check and credit bureau report, if available.Amount spent on information Number of Rupees spent (from a maximum of 18) per evaluated loan file. Only defined

when loan officers were charged for non-basic loan file sections.Internal rating Internal rating assigned to loan l by loan officer i. This measure is normalized by

subtracting the mean of the “control group,” and dividing by the standard deviation

of the “control group.” The control group is defined as those receiving low-powered

incentives [20,0,10]Personal and management risk Sum of all sub-components of the internal rating pertaining to the applicant’s personal

and management risk, normalized as described above.Business and financial risk Sum of all sub-components of the internal rating pertaining to the applicant’s business

and financial risk, normalized as described above.Overall rating (baseline mean) Mean of all risk ratings assigned to loan l in all evaluations under the “Baseline”

treatment.Personal and management risk

(baseline mean)

Mean of all risk ratings capturing personal and management risk, assigned to loan l

in all evaluations under the “Baseline” treatment.

Business and financial risk

(baseline mean)

Mean of all risk ratings capturing business and financial risk, assigned to loan l in all

evaluations under the “Baseline” treatment.

Approved Dummy equal to 1 if a loan evaluated in the experiment was approved.

Profit per approved loan Profit per approved loan from the viewpoint of the lender, measured as the discounted

stream of payments and origination fee minus the initial disbursement amount. Loans

turned down in the experiment are excluded.Profit per screened loan Profit per approved loan from the viewpoint of the lender, measured as the discounted

stream of payments and origination fee minus the initial disbursement amount. For

loans turned down in the experiment, the profit is equal to 0.

60

B Appendix Tables

Table OA.I: Test of Random AssignmentThis table presents a test of random assignment across the four main treatments. We regress treatmentdummies on demographic variables, controlling for randomization strata, lab and week fixed effects. Ageis the loan officer’s age in years, Male is a dummy variable taking a value of 1 if the participant is male.Rank is the loan officer’s level of seniority in the bank. Experience is the number of years the loan officerhas been employed by the bank. Branch Manager is a dummy variable indicating whether the participanthas ever served as a branch manager. * p<0.10 ** p<0.05 *** p<0.01.

Incentive Treatment

High-powered Origination bonus

(1) (2)

Male 0.006 -0.017

(0.03) (0.03)

Age -0.002 -0.001

(0.002) (0.002)

Education [Master’s degree] -0.031 0.014

(0.019) (0.020)

Experience [Years] 0.002 0.001

(0.001) (0.001)

Rank [1 Lowest - 5 Highest] -0.005 -0.009

(0.008) (0.008)

Branch manager experience -0.007 -0.012

(0.023) (0.024)

Number of observations 9,268 9,806

R-squared, adjusted 0.314 0.322

61

Table OA.II: Test for Learning During the ExperimentThis table presents a formal test for the presence of learning effects during theexperiment. The dependent variable in column (1) is a dummy variable taking ona value of one for a correct lending decision, defined as approving a performingloan or declining a non-performing loan. The dependent variable in column (2)is the profit per loan for the sample of approved loans, denominated in US$ ’000,The dependent variable in column (3) is the profit per loans for the total sampleof screened loans in units of US$ ’000. * p<0.10 ** p<0.05 *** p<0.01.

Lending decisions Profit per loan

correct approved screened

(1) (2) (3)

Number of experimental -0.002* 0.003 -0.003

sessions completed (0.00) (0.00) (0.00)

Loan fixed effects Yes No No

Loan officer fixed effects Yes Yes Yes

Number of observations 14,675 9,357 13,084

R-squared 0.322 0.652 0.415

62

Table OA.III: Predictive Content of Internal RatingsThis table presents evidence on the predictive content of internal ratings. The dependent variable incolumn (1) is a dummy equal to 1 if a loan was approved by the reviewing loan officer and 0 otherwise.The dependent variable in column (2) is a dummy equal to 1 if a loan performed and 0 otherwise.In column (3) the dependent variable is the profit per loan of approved loans, denominated in unitsof US$ ’000. The dependent variable in column (4) is the profit per screened loan, denominated inunits of US$ ’000. Each regression includes controls for the incentive treatment conditions and thenumber of experimental sessions completed by the reviewing loan officer. * p<0.10 ** p<0.05 ***p<0.01.

Lending Performance Profit per loan

Approved=1 Performing=1 approved screened

(1) (2) (3) (4)

Panel A: Final Rating

Log internal rating 1.348*** 0.322*** 0.659*** 0.305***

(0.04) (0.03) (0.19) (0.05)

Number of observations 13,979 13,979 8,834 12,411

R-squared 0.443 0.064 0.03 0.024

Panel B: Personal and Management Risk

Log internal rating 1.159*** 0.279*** 0.476*** 0.251***

Personal and management risk (0.04) (0.03) (0.17) (0.06)

Number of observations 13979 13979 8834 12,411

R-squared 0.368 0.061 0.03 0.023

Panel C: Business and Financial Risk

Log internal rating 1.265*** 0.318*** 0.572*** 0.282***

Business and financial risk (0.04) (0.02) (0.18) (0.05)

Number of observations 13,979 13,979 8,834 12,411

R-squared 0.439 0.066 0.03 0.024

Loan fixed effects No No No No

Loan officer fixed effects Yes Yes Yes Yes

63

Table OA.IV: The Effect of Incentives on EffortThis table reports treatment effects of performance pay on screening effort. Each column reports resultsfrom a separate regression. The omitted treatment category is the low-powered baseline incentive. Thedependent variable in column (1) and (2) is number of sections of the loan file viewed; the dependentvariable in Columns (3) and (4) is the number of loan file sections reviewed when loan officers wererequired to pay for additional information. The regressions in Columns (1) and (2) include the entiresample, while Columns (3) and (4) limit the data to evaluations to the “costly information setting.”All regressions include a lab fixed effect, randomization stratum and week fixed effects, as well asdummies to control for treatment conditions not reported in this table. Loan officer controls includeage, seniority, rank, education, and indicators for branch manager and business experience. Standarderrors, in parentheses, are clustered at the loan officer × session level. * p<0.10 ** p<0.05 *** p<0.01.

Free information Costly information

Loan file Loan file

sections reviewed sections reviewed

(1) (2) (3) (4)

Baseline, omitted

High-powered 0.488* 0.332** 1.225*** 0.794***

(0.28) (0.15) (0.42) (0.25)

Origination bonus 0.125 -0.135 -0.147 -0.156

(0.28) (0.16) (0.40) (0.21)

Performance bonus low -0.054 -0.101 0.550 0.131

(0.29) (0.21) (0.38) (0.22)

Performance bonus high -0.018 0.080 0.175 -0.084

(0.32) (0.25) (0.32) (0.16)

Loan fixed effects No Yes No Yes

Loan officer fixed effects No Yes No Yes

Loan officer controls Yes No Yes No

Number of observations 5,885 5,987 8,520 8,688

R-squared, adjusted 0.232 0.689 0.271 0.725

64

Table OA.V: The Effect of Incentives on Risk-AssessmentThis table reports the effect of performance pay on loan officers’ subjective assessment of creditrisk. Each column shows results from a separate regression. The omitted treatment categoryis the low-powered baseline incentive. The dependent variable in regressions (1) and (2) is theoverall risk rating, standardized to have mean zero. The dependent variable in Columns (3) and(4) is the normalized sub-rating for all categories that pertain to the personal risk of a potentialapplicant. In Columns (5) and (6) the dependent variable is the normalized sub-rating for allrating categories that pertain to the business, management and financial risk of a loan applicant.All regressions include a lab fixed effect, randomization stratum and week fixed effects, as wellas dummies to control for treatment conditions not reported in this table. Loan officer controlsinclude age, seniority, rank, education, and indicators for branch manager and business experience.Standard errors are clustered at the loan officer × session level. * p<0.10 ** p<0.05 *** p<0.01.

Internal risk-rating

Overall rating Personal and Business and

management risk financial risk

(1) (2) (3) (4) (5) (6)

Baseline, omitted

High-powered 0.029 0.006 0.011 -0.001 0.054 0.02

(0.09) (0.04) (0.09) (0.04) (0.09) (0.04)

Origination bonus 0.144* 0.006 0.130* -0.015 0.156** 0.021

(0.08) (0.04) (0.08) (0.04) (0.08) (0.04)

Performance bonus low 0.044 0.157*** 0.024 0.141** 0.055 0.142**

(0.11) (0.06) (0.11) (0.06) (0.11) (0.06)

Performance bonus high 0.267** 0.298*** 0.247** 0.285*** 0.266** 0.275***

(0.12) (0.06) (0.12) (0.06) (0.11) (0.06)

Loan fixed effects No Yes No Yes No Yes

Loan officer fixed effects No Yes No Yes No Yes

Loan officer controls Yes No Yes No Yes No

Number of observations 14,405 14,675 14,405 14,675 14,405 14,675

R-squared, adjusted 0.151 0.64 0.142 0.644 0.161 0.626

65

TableOA.V

I:The

Effectof

Incentives

onRisk

-Tak

ing

Thistablerepo

rtstreatm

enteff

ects

ofpe

rforman

cepa

yon

risk-tak

ing.

Eachcolumnrepo

rtsresultsfrom

asepa

rate

regression

.The

omittedtreatm

entcategory

isthe

low-pow

ered

baselin

eincentive.

The

depe

ndentvariab

lein

regression

s(1)an

d(2)is

themeanno

rmalized

risk-ratingacross

allr

isk-rating

categories.In

Colum

ns(3)an

d(4)thedepe

ndentvariab

leis

theaverag

erating

,across

allloan

officers,givento

aloan

filewhenevalua

tedun

dertheba

selin

eincentivescheme.

InColum

ns(5)an

d(6)

thedepe

ndentvariab

leis

theno

rmalized

meanrisk-ratingforallr

atingqu

estion

srelating

toan

applican

t’sbu

siness,m

anagem

entan

dfin

ancial

risk.To

capturethedegree

ofex-anteun

certaintyab

outthequ

ality

ofaloan

,Colum

ns(7)to

(12)

repe

attheexercise

usingthecoeffi

cientof

variationof

risk-ratings

assign

edto

agivenloan

under

theba

selin

etreatm

entas

thedepe

ndentvariab

le.Allregression

sinclud

ealabfix

edeff

ect,

rand

omizationstratum

andweekfix

edeff

ects,a

swella

sdu

mmiesto

controlfor

treatm

entcond

itions

notrepo

rted

inthis

table.

Loan

officercontrols

includ

eage,

seniority

,rank

,education,

andindicators

forbran

chman

ager

andbu

siness

expe

rience.

Stan

dard

errors,inpa

rentheses,

areclusteredat

theloan

officer×

sessionlevel.*p<

0.10

**p<

0.05

***p<

0.01.

Perceived

qualityof

approved

loan

sPerceived

loan

qualityof

approved

loan

s

[Meanrating

]a[Coefficientof

variation]

b

Overallrating

Persona

land

Businessan

dOverallrating

Persona

land

Businessan

d

man

agem

entrisk

finan

cial

risk

man

agem

entrisk

finan

cial

risk

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Baseline,

omitted

High-po

wered

-0.039

-0.046

-0.024

-0.03

-0.058*

-0.065*

-0.015***

-0.015***

-0.018***

-0.018***

-0.013**

-0.013**

(0.03)

(0.03)

(0.03)

(0.03)

(0.03)

(0.03)

(0.01)

(0.01)

(0.01)

(0.01)

(0.01)

(0.01)

Origina

tion

bonu

s0.027

0.02

50.031

0.032

0.025

0.022

-0.008*

-0.007

-0.007

-0.006

-0.010**

-0.009*

(0.03)

(0.03)

(0.03)

(0.03)

(0.03)

(0.03)

(0.00)

(0.00)

(0.00)

(0.00)

(0.00)

(0.00)

Perform

ance

bonu

slow

-0.047

-0.056

-0.038

-0.045

-0.04

-0.049

-0.006

-0.004

-0.008

-0.006

-0.005

-0.002

(0.04)

(0.04)

(0.04)

(0.04)

(0.04)

(0.04)

(0.01)

(0.01)

(0.01)

(0.01)

(0.01)

(0.01)

Perform

ance

bonu

shigh

-0.044

-0.059

-0.035

-0.044

-0.034

-0.052

-0.003

-0.002

0.004

0.005

-0.008

-0.006

(0.05)

(0.05)

(0.04)

(0.05)

(0.04)

(0.04)

(0.01)

(0.01)

(0.01)

(0.01)

(0.01)

(0.01)

Loan

fixed

effects

No

No

No

No

No

No

No

No

No

No

No

No

Loan

officerfix

edeff

ects

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

Loan

officercontrols

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Num

berof

observations

10,180

10,402

10,180

10,402

10,180

10,402

9,349

9,555

9,349

9,555

9,349

9,555

R-squ

ared,a

djusted

0.08

0.096

0.07

0.087

0.082

0.098

0.06

0.081

0.062

0.084

0.062

0.082

[a]M

eanrating

assign

edto

loan

application

lby

allloa

noffi

cers

evalua

ting

theloan

undertheba

selin

etreatm

ent.

[b]C

oefficientof

variationof

rating

sassigedto

loan

application

lby

allloanoffi

cers

review

ingtheloan

undertheba

selin

etreatm

ent.

66

TableOA.V

II:Incentiv

es,L

ending

Decision

san

dPr

ofit

Thistablerepo

rtstheeff

ectof

performan

cepa

yon

loan

approvalsan

dtheprofi

tabilityof

lend

ing.

Eachcolumnrepo

rtsresultsfrom

asepa

rate

regression

.The

omittedtreatm

ent

catego

ryis

thelow-pow

ered

baselin

eincentive.

The

depe

ndentvariab

lein

Colum

ns(1)to

(8)is

adu

mmyequa

ltoon

eforloan

sap

proved

byan

expe

rimentalp

articipa

ntan

dzero

otherw

ise.

The

estimates

inColum

ns(1)an

d(2)areba

sedon

thefullsample.

Estim

ates

incolumns

(3)an

d(4)areba

sedon

thesampleof

performingloan

s,estimates

inColum

ns(5)an

d(6)areba

sedon

thesampleof

non-pe

rformingloan

s,an

destimates

inColum

ns(7)an

d(8)areba

sedon

thesampleof

loan

sthat

wereinitially

declined

bytheLe

nder.Colum

ns(9)to

(12)

repo

rttreatm

entestimates

ofincentives

onprofi

tpe

rap

proved

loan

andprofi

tpe

rscreened

loan

,inun

itsof

US$

’000.Allregression

sinclud

ealabfix

edeff

ect,rand

omizationstratum

andweekfix

edeff

ects,a

swella

sdu

mmiesto

controlfor

treatm

entcond

itions

notrepo

rted

inthis

table.

Loan

officercontrols

includ

eag

e,seniority

,ran

k,education,

andindicators

forbran

chman

ager

andbu

siness

expe

rience.Stan

dard

errors,inpa

rentheses,

areclusteredat

theloan

officer×

sessionlevel.

*p<

0.10

**p<

0.05

***p<

0.01

.

Pan

elA:App

roved

Pan

elB:Profit

Total

Perform

ing

Non

-perform

ing

Declin

edby

bank

perap

proved

loan

perscreened

loan

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Baseline,

omitted

High-po

wered

-0.036

*-0.004

-0.010

0.015

-0.110**

-0.063

-0.042

-0.014

148.986*

175.907**

84.900

114.930*

(0.02)

(0.02)

(0.03)

(0.03)

(0.06)

(0.06)

(0.06)

(0.06)

(85.01)

(86.81)

(62.51)

(63.76)

Origina

tion

bonu

s0.083*

**0.07

9***

0.087***

0.068***

0.048

0.082*

0.098*

0.102*

29.489

-4.182

80.193

56.500

(0.02)

(0.02)

(0.02)

(0.02)

(0.05)

(0.05)

(0.06)

(0.05)

(78.04)

(79.02)

(60.96)

(61.21)

Perform

ance

bonu

slow

0.09

0***

0.13

5***

0.074**

0.094**

0.099

0.201**

0.227**

0.206**

109.190

88.969

153.805*

129.370

(0.03)

(0.03)

(0.04)

(0.04)

(0.08)

(0.09)

(0.09)

(0.09)

(97.89)

(103.42)

(81.12)

(85.54)

Perform

ance

bonu

shigh

0.12

2***

0.15

4***

0.124***

0.116***

0.101

0.182**

0.196**

0.239**

59.263

20.417

120.116

55.527

(0.04)

(0.03)

(0.04)

(0.04)

(0.08)

(0.08)

(0.10)

(0.10)

(109.21)

(123.52)

(90.89)

(103.86)

Loan

fixed

effects

No

Yes

No

Yes

No

Yes

No

Yes

No

No

No

No

Loan

officerfix

edeff

ects

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

Loan

officercontrols

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Num

berof

observations

14,405

14,675

9,398

9,575

2,730

2,778

2,277

2,322

9,242

9,435

11,853

12,074

R-squ

ared,a

djusted

0.02

50.21

20.025

0.203

0.054

0.244

0.080

0.300

0.009

0.020

0.007

0.016

67